From 1e59781fb477961146857460b9506d92a5bc6492 Mon Sep 17 00:00:00 2001 From: dberkin1 Date: Fri, 13 Feb 2026 20:52:24 +0300 Subject: [PATCH 01/15] Add TTS evals --- src/openbench/cli/commands/evaluate.py | 21 ++ src/openbench/dataset/__init__.py | 3 + src/openbench/dataset/dataset_aliases.py | 14 + src/openbench/dataset/dataset_registry.py | 2 + .../dataset/dataset_speech_generation.py | 110 ++++++ .../word_error_metrics/word_error_metrics.py | 1 + src/openbench/pipeline/__init__.py | 1 + src/openbench/pipeline/pipeline_aliases.py | 26 ++ .../pipeline/speech_generation/__init__.py | 12 + .../pipeline/speech_generation/common.py | 110 ++++++ .../speech_generation_wkp.py | 316 ++++++++++++++++++ src/openbench/runner/benchmark.py | 2 + src/openbench/types.py | 1 + 13 files changed, 619 insertions(+) create mode 100644 src/openbench/dataset/dataset_speech_generation.py create mode 100644 src/openbench/pipeline/speech_generation/__init__.py create mode 100644 src/openbench/pipeline/speech_generation/common.py create mode 100644 src/openbench/pipeline/speech_generation/speech_generation_wkp.py diff --git a/src/openbench/cli/commands/evaluate.py b/src/openbench/cli/commands/evaluate.py index 1b4f5f7..c35eb01 100644 --- a/src/openbench/cli/commands/evaluate.py +++ b/src/openbench/cli/commands/evaluate.py @@ -176,6 +176,7 @@ def run_alias_mode( wandb_tags: list[str] | None, use_keywords: bool | None, force_language: bool, + pipeline_config: list[str] | None, verbose: bool, ) -> BenchmarkResult: """Run evaluation using pipeline and dataset aliases.""" @@ -208,6 +209,19 @@ def run_alias_mode( if verbose: typer.echo("✅ Force language: enabled") + # Handle generic pipeline config overrides (key=value pairs) + if pipeline_config: + for item in pipeline_config: + if "=" not in item: + raise typer.BadParameter( + f"Invalid --pipeline-config format: '{item}'. " + f"Expected key=value" + ) + key, value = item.split("=", 1) + pipeline_config_override[key] = value + if verbose: + typer.echo(f"✅ Config override: {key}={value}") + pipeline = PipelineRegistry.create_pipeline(pipeline_name, config=pipeline_config_override) ######### Build Benchmark Config ######### @@ -345,6 +359,12 @@ def evaluate( "--force-language", help="Force language hinting for compatible pipelines", ), + pipeline_config: list[str] | None = typer.Option( + None, + "--pipeline-config", + "-pc", + help="Override pipeline config values as key=value pairs (e.g. --pipeline-config speaker=serena)", + ), verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output"), ) -> None: """Run evaluation benchmarks. @@ -406,6 +426,7 @@ def evaluate( wandb_tags=wandb_tags, use_keywords=use_keywords, force_language=force_language, + pipeline_config=pipeline_config, verbose=verbose, ) display_result(result) diff --git a/src/openbench/dataset/__init__.py b/src/openbench/dataset/__init__.py index 8c90fae..540942e 100644 --- a/src/openbench/dataset/__init__.py +++ b/src/openbench/dataset/__init__.py @@ -6,6 +6,7 @@ from .dataset_diarization import DiarizationDataset, DiarizationSample from .dataset_orchestration import OrchestrationDataset, OrchestrationSample from .dataset_registry import DatasetRegistry +from .dataset_speech_generation import SpeechGenerationDataset, SpeechGenerationSample from .dataset_streaming_transcription import StreamingDataset, StreamingSample from .dataset_transcription import TranscriptionDataset, TranscriptionSample @@ -24,11 +25,13 @@ "TranscriptionDataset", "StreamingDataset", "OrchestrationDataset", + "SpeechGenerationDataset", # Sample types "DiarizationSample", "TranscriptionSample", "StreamingSample", "OrchestrationSample", + "SpeechGenerationSample", # Registry "DatasetRegistry", ] diff --git a/src/openbench/dataset/dataset_aliases.py b/src/openbench/dataset/dataset_aliases.py index c37af42..9a7ad1b 100644 --- a/src/openbench/dataset/dataset_aliases.py +++ b/src/openbench/dataset/dataset_aliases.py @@ -554,6 +554,20 @@ def register_dataset_aliases() -> None: description="Common Voice dataset for transcription evaluation with up to 400 samples per language this subset contains only russian", ) + ########## SPEECH GENERATION ########## + + DatasetRegistry.register_alias( + "customer-service-tts-prompts-vocalized", + DatasetConfig( + dataset_id="argmaxinc/customer-service-tts-prompts-vocalized", + split="validation", + ), + supported_pipeline_types={ + PipelineType.SPEECH_GENERATION, + }, + description="Customer service TTS prompts with vocalized audio for speech generation evaluation.", + ) + ########## STREAMING TRANSCRIPTION ########## DatasetRegistry.register_alias( diff --git a/src/openbench/dataset/dataset_registry.py b/src/openbench/dataset/dataset_registry.py index f73ae17..1b40e3e 100644 --- a/src/openbench/dataset/dataset_registry.py +++ b/src/openbench/dataset/dataset_registry.py @@ -8,6 +8,7 @@ from .dataset_base import BaseDataset, DatasetConfig from .dataset_diarization import DiarizationDataset from .dataset_orchestration import OrchestrationDataset +from .dataset_speech_generation import SpeechGenerationDataset from .dataset_streaming_transcription import StreamingDataset from .dataset_transcription import TranscriptionDataset @@ -139,3 +140,4 @@ def has_alias(cls, alias: str) -> bool: DatasetRegistry.register(PipelineType.ORCHESTRATION, OrchestrationDataset) DatasetRegistry.register(PipelineType.STREAMING_TRANSCRIPTION, StreamingDataset) DatasetRegistry.register(PipelineType.TRANSCRIPTION, TranscriptionDataset) +DatasetRegistry.register(PipelineType.SPEECH_GENERATION, SpeechGenerationDataset) diff --git a/src/openbench/dataset/dataset_speech_generation.py b/src/openbench/dataset/dataset_speech_generation.py new file mode 100644 index 0000000..d9e2748 --- /dev/null +++ b/src/openbench/dataset/dataset_speech_generation.py @@ -0,0 +1,110 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +import numpy as np +from pydantic import Field +from typing_extensions import TypedDict + +from ..pipeline_prediction import Transcript +from .dataset_base import BaseDataset, BaseSample + + +class SpeechGenerationExtraInfo(TypedDict, total=False): + """Extra info for speech generation samples.""" + + language: str + + +class SpeechGenerationRow(TypedDict): + """Expected row structure for speech generation. + + Requires 'text' (the prompt string). No audio needed. + """ + + text: str + + +class SpeechGenerationSample( + BaseSample[Transcript, SpeechGenerationExtraInfo] +): + """Sample for speech generation tasks. + + The reference Transcript is created from the text + prompt. The pipeline generates audio from this text + and transcribes it to compute WER against reference. + """ + + generated_audio_duration: float | None = Field( + default=None, + description=( + "Duration (seconds) of the TTS-generated audio. " + "Set by the pipeline after generation." + ), + ) + + def get_audio_duration(self) -> float: + """Return generated audio duration if available. + + Falls back to the dummy waveform calculation + when the pipeline hasn't set the real duration yet. + """ + if self.generated_audio_duration is not None: + return self.generated_audio_duration + return super().get_audio_duration() + + @property + def text(self) -> str: + """The original text prompt.""" + return self.reference.get_transcript_string() + + +class SpeechGenerationDataset( + BaseDataset[SpeechGenerationSample] +): + """Dataset for speech generation pipelines. + + Expects column: 'text' (the prompt string). + No audio column is required since audio is generated + by the pipeline itself. + """ + + _expected_columns = ["text"] + _sample_class = SpeechGenerationSample + + def _extract_audio_info( + self, row: dict + ) -> tuple[str, np.ndarray, int]: + """Override to provide dummy audio info. + + Speech generation datasets don't have input audio. + We provide a placeholder waveform so the framework + sample structure is satisfied. The pipeline ignores + the waveform entirely. + """ + audio_name = f"sample_{row['idx']}" + # Use audio_name from the row if available + if "audio_name" in row and row["audio_name"]: + audio_name = str(row["audio_name"]) + dummy_waveform = np.zeros(1, dtype=np.float32) + dummy_sample_rate = 16000 + return audio_name, dummy_waveform, dummy_sample_rate + + def prepare_sample( + self, row: SpeechGenerationRow + ) -> tuple[Transcript, SpeechGenerationExtraInfo]: + """Prepare reference from dataset row. + + Splits text prompt into words to create the + reference Transcript. + """ + text = row["text"] + words = text.split() + reference = Transcript.from_words_info( + words=words, + ) + + extra_info: SpeechGenerationExtraInfo = {} + if "language" in row: + extra_info["language"] = row["language"] + + return reference, extra_info diff --git a/src/openbench/metric/word_error_metrics/word_error_metrics.py b/src/openbench/metric/word_error_metrics/word_error_metrics.py index 4cef2a1..24655d5 100644 --- a/src/openbench/metric/word_error_metrics/word_error_metrics.py +++ b/src/openbench/metric/word_error_metrics/word_error_metrics.py @@ -223,6 +223,7 @@ def compute_metric(self, detail: Details) -> float: PipelineType.TRANSCRIPTION, PipelineType.ORCHESTRATION, PipelineType.STREAMING_TRANSCRIPTION, + PipelineType.SPEECH_GENERATION, ), MetricOptions.WER, ) diff --git a/src/openbench/pipeline/__init__.py b/src/openbench/pipeline/__init__.py index 598328b..268d0d2 100644 --- a/src/openbench/pipeline/__init__.py +++ b/src/openbench/pipeline/__init__.py @@ -6,6 +6,7 @@ from .diarization import * from .orchestration import * from .pipeline_registry import PipelineRegistry +from .speech_generation import * from .streaming_transcription import * from .transcription import * diff --git a/src/openbench/pipeline/pipeline_aliases.py b/src/openbench/pipeline/pipeline_aliases.py index 97287b0..770af12 100644 --- a/src/openbench/pipeline/pipeline_aliases.py +++ b/src/openbench/pipeline/pipeline_aliases.py @@ -25,6 +25,9 @@ WhisperXPipeline, ) from .pipeline_registry import PipelineRegistry +from .speech_generation import ( + WhisperKitSpeechGenerationPipeline, +) from .streaming_transcription import ( AssemblyAIStreamingPipeline, DeepgramStreamingPipeline, @@ -642,6 +645,29 @@ def register_pipeline_aliases() -> None: description="PyannoteAI transcription pipeline (ignores speaker attribution). Uses the precision-2 model with Nvidia Parakeet STT. Requires `PYANNOTE_TOKEN` env var from https://www.pyannote.ai/.", ) + ################# SPEECH GENERATION PIPELINES ################# + + PipelineRegistry.register_alias( + "whisperkit-speech-generation", + WhisperKitSpeechGenerationPipeline, + default_config={ + "out_dir": "./speech_generation_results", + "cli_path": os.getenv("WHISPERKIT_CLI_PATH"), + "speaker": "aiden", + "language": "english", + "seed": 10, + "temperature": 0.9, + "top_k": 50, + "max_new_tokens": 245, + "transcription_cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"), + "transcription_repo_id": "argmaxinc/parakeetkit-pro", + "transcription_model_variant": "nvidia_parakeet-v2_476MB", + }, + description="WhisperKit speech generation pipeline. Generates audio from text prompts using whisperkit-cli TTS, " + "then transcribes the generated audio to compute WER against the original prompt. " + "Requires `WHISPERKIT_CLI_PATH` env var pointing to the whisperkit-cli binary.", + ) + ################# STREAMING TRANSCRIPTION PIPELINES ################# PipelineRegistry.register_alias( diff --git a/src/openbench/pipeline/speech_generation/__init__.py b/src/openbench/pipeline/speech_generation/__init__.py new file mode 100644 index 0000000..1a6bce7 --- /dev/null +++ b/src/openbench/pipeline/speech_generation/__init__.py @@ -0,0 +1,12 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +from .common import SpeechGenerationConfig, SpeechGenerationOutput +from .speech_generation_wkp import WhisperKitSpeechGenerationPipeline + + +__all__ = [ + "SpeechGenerationConfig", + "SpeechGenerationOutput", + "WhisperKitSpeechGenerationPipeline", +] diff --git a/src/openbench/pipeline/speech_generation/common.py b/src/openbench/pipeline/speech_generation/common.py new file mode 100644 index 0000000..96b924e --- /dev/null +++ b/src/openbench/pipeline/speech_generation/common.py @@ -0,0 +1,110 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +from pydantic import Field + +from ...pipeline_prediction import Transcript +from ..base import PipelineConfig, PipelineOutput + + +class SpeechGenerationConfig(PipelineConfig): + """Base config for speech generation pipelines.""" + + cli_path: str = Field( + ..., + description=( + "Path to the whisperkit-cli binary " + "(used for TTS generation)." + ), + ) + + # TTS parameters + speaker: str = Field( + default="aiden", + description="Speaker voice for TTS generation.", + ) + language: str = Field( + default="english", + description="Language for TTS generation.", + ) + seed: int | None = Field( + default=None, + description="Random seed for reproducible output.", + ) + temperature: float = Field( + default=0.9, + description="Sampling temperature for TTS.", + ) + top_k: int = Field( + default=50, + description="Top-k sampling for TTS.", + ) + max_new_tokens: int = Field( + default=245, + description="Max RVQ frames to generate.", + ) + models_path: str | None = Field( + default=None, + description="Local model directory for TTS.", + ) + model_repo: str | None = Field( + default=None, + description="HF repo for TTS model download.", + ) + version_dir: str | None = Field( + default=None, + description="TTS model version directory.", + ) + tokenizer: str | None = Field( + default=None, + description="HF tokenizer repo or local path.", + ) + + # Transcription parameters + transcription_cli_path: str | None = Field( + default=None, + description=( + "Path to CLI for transcription. " + "Defaults to cli_path if not set." + ), + ) + transcription_repo_id: str | None = Field( + default=None, + description=( + "HuggingFace repo ID for transcription " + "model (e.g. argmaxinc/parakeetkit-pro)." + ), + ) + transcription_model_variant: str | None = Field( + default=None, + description=( + "Model variant folder within the repo " + "(e.g. nvidia_parakeet-v2_476MB)." + ), + ) + transcription_model_path: str | None = Field( + default=None, + description=( + "Local path to ASR model dir. " + "Overrides repo_id/model_variant." + ), + ) + transcription_word_timestamps: bool = Field( + default=True, + description="Include word timestamps.", + ) + transcription_chunking_strategy: str = Field( + default="vad", + description="Chunking strategy (none or vad).", + ) + + +class SpeechGenerationOutput(PipelineOutput[Transcript]): + """Output for speech generation pipelines. + + The prediction is a Transcript of the generated audio + (obtained by transcribing the TTS output). WER is + computed against the original text prompt. + """ + + pass diff --git a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py new file mode 100644 index 0000000..d7191eb --- /dev/null +++ b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py @@ -0,0 +1,316 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +""" +Speech generation pipeline using WhisperKit CLI. + +Generates TTS audio from text prompts, then transcribes +the generated audio back to text for WER evaluation +against the original prompt. +""" + +import json +import subprocess +import time +from pathlib import Path +from typing import Callable + +from argmaxtools.utils import get_logger +from pydantic import BaseModel, Field + +from ...dataset.dataset_base import BaseSample +from ...dataset.dataset_speech_generation import ( + SpeechGenerationSample, +) +from ...engine.whisperkitpro_engine import ( + WhisperKitPro, + WhisperKitProConfig, + WhisperKitProInput, +) +from ...pipeline_prediction import Transcript +from ..base import ( + Pipeline, + PipelineOutput, + PipelineType, + register_pipeline, +) +from .common import SpeechGenerationConfig, SpeechGenerationOutput + + +logger = get_logger(__name__) + +TEMP_TTS_AUDIO_DIR = Path("./temp_tts_audio") + + +class SpeechGenerationInput(BaseModel): + """Input for the speech generation pipeline.""" + + text: str = Field( + ..., + description="Text prompt to generate speech from.", + ) + audio_name: str = Field( + ..., + description=( + "Unique identifier for this sample " + "(used for temp file naming)." + ), + ) + + +@register_pipeline +class WhisperKitSpeechGenerationPipeline(Pipeline): + """Speech generation pipeline using WhisperKit CLI. + + This pipeline: + 1. Generates audio from text via whisperkit-cli tts + 2. Transcribes audio via WhisperKitPro engine + 3. Returns transcription as Transcript for WER eval + 4. Cleans up temporary audio and report files + """ + + _config_class = SpeechGenerationConfig + pipeline_type = PipelineType.SPEECH_GENERATION + + def build_pipeline( + self, + ) -> Callable[[SpeechGenerationInput], Transcript]: + config = self.config + pipeline_ref = self + + # Build the WhisperKitPro engine for transcription + # (downloads model once, reuses for all samples) + transcription_engine = self._build_transcription_engine() + + def generate_and_transcribe( + input: SpeechGenerationInput, + ) -> Transcript: + TEMP_TTS_AUDIO_DIR.mkdir( + parents=True, exist_ok=True + ) + + audio_path = ( + TEMP_TTS_AUDIO_DIR + / f"{input.audio_name}.wav" + ) + + # -- Step 1: Generate audio via TTS -- + tts_cmd = [ + config.cli_path, + "tts", + "--text", input.text, + "--speaker", config.speaker, + "--language", config.language, + "--output-path", str(audio_path), + "--temperature", str(config.temperature), + "--top-k", str(config.top_k), + "--max-new-tokens", + str(config.max_new_tokens), + ] + + if config.seed is not None: + tts_cmd.extend( + ["--seed", str(config.seed)] + ) + if config.models_path is not None: + tts_cmd.extend( + ["--models-path", config.models_path] + ) + if config.model_repo is not None: + tts_cmd.extend( + ["--model-repo", config.model_repo] + ) + if config.version_dir is not None: + tts_cmd.extend( + ["--version-dir", config.version_dir] + ) + if config.tokenizer is not None: + tts_cmd.extend( + ["--tokenizer", config.tokenizer] + ) + + logger.debug( + f"Running TTS: {' '.join(tts_cmd)}" + ) + + tts_result = subprocess.run( + tts_cmd, capture_output=True, text=True + ) + + if tts_result.returncode != 0: + raise RuntimeError( + "whisperkit-cli tts failed " + f"(exit {tts_result.returncode}):\n" + f" stdout: " + f"{tts_result.stdout[:500]}\n" + f" stderr: " + f"{tts_result.stderr[:500]}" + ) + + if not audio_path.exists(): + raise RuntimeError( + "TTS completed but audio file " + f"not found at {audio_path}" + ) + + logger.info( + f"Generated TTS audio: {audio_path}" + ) + + # -- Step 2: Read audio duration before + # transcription (engine may delete file) -- + try: + import soundfile as sf + info = sf.info(str(audio_path)) + pipeline_ref._last_generated_duration = ( + info.duration + ) + except Exception as e: + logger.warning( + f"WAV duration read failed: {e}" + ) + pipeline_ref._last_generated_duration = ( + None + ) + + # -- Step 3: Transcribe via WhisperKitPro -- + engine_input = WhisperKitProInput( + audio_path=audio_path, + keep_audio=False, + ) + engine_output = transcription_engine( + engine_input + ) + + # -- Step 4: Parse transcription report -- + json_path = engine_output.json_report_path + if json_path.exists(): + with json_path.open("r") as f: + data = json.load(f) + all_words, all_starts, all_ends = ( + [], [], [], + ) + for seg in data.get("segments", []): + for w in seg.get("words", []): + all_words.append(w["word"]) + if "start" in w: + all_starts.append( + w["start"] + ) + if "end" in w: + all_ends.append(w["end"]) + transcript = Transcript.from_words_info( + words=all_words, + start=( + all_starts + if all_starts else None + ), + end=( + all_ends + if all_ends else None + ), + ) + # Clean up report files + json_path.unlink(missing_ok=True) + srt_path = engine_output.srt_report_path + if srt_path: + srt_path.unlink(missing_ok=True) + else: + raise RuntimeError( + "Transcription report not found " + f"at {json_path}" + ) + + logger.info( + "Transcription: " + + transcript.get_transcript_string( + )[:100] + + "..." + ) + + return transcript + + return generate_and_transcribe + + def _build_transcription_engine(self) -> WhisperKitPro: + """Create WhisperKitPro engine for transcription. + + Uses the same engine as the dedicated + WhisperKitPro transcription pipelines, which + handles model download, caching, and CLI args. + """ + config = self.config + cli_path = ( + config.transcription_cli_path or config.cli_path + ) + + import coremltools as ct + engine_config = WhisperKitProConfig( + repo_id=config.transcription_repo_id, + model_variant=config.transcription_model_variant, + model_dir=config.transcription_model_path, + word_timestamps=config.transcription_word_timestamps, + chunking_strategy=config.transcription_chunking_strategy, + audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, + text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, + ) + + return WhisperKitPro( + cli_path=cli_path, + transcription_config=engine_config, + ) + + def __call__( + self, input_sample: BaseSample + ) -> PipelineOutput: + """Run pipeline and set generated audio duration. + + Overrides base __call__ to propagate the real + TTS audio duration back onto the sample so the + runner reports accurate audio_duration and + speed_factor values. + """ + self._last_generated_duration: float | None = None + parsed_input = self.parse_input(input_sample) + start_time = time.perf_counter() + output = self.pipeline(parsed_input) + end_time = time.perf_counter() + prediction_time = end_time - start_time + parsed_output = self.parse_output(output) + if parsed_output.prediction_time is None: + parsed_output.prediction_time = prediction_time + + # Propagate generated audio duration to sample + dur = self._last_generated_duration + logger.info( + f"Generated audio duration: {dur}s" + ) + if ( + isinstance(input_sample, SpeechGenerationSample) + and dur is not None + ): + input_sample.generated_audio_duration = dur + logger.info( + "Set sample duration to " + f"{input_sample.generated_audio_duration}s" + ) + + return parsed_output + + def parse_input( + self, input_sample: SpeechGenerationSample + ) -> SpeechGenerationInput: + """Extract text prompt from the sample.""" + text = ( + input_sample.reference.get_transcript_string() + ) + return SpeechGenerationInput( + text=text, + audio_name=input_sample.audio_name, + ) + + def parse_output( + self, output: Transcript + ) -> SpeechGenerationOutput: + """Wrap transcription into output.""" + return SpeechGenerationOutput(prediction=output) diff --git a/src/openbench/runner/benchmark.py b/src/openbench/runner/benchmark.py index 439c15c..b195862 100644 --- a/src/openbench/runner/benchmark.py +++ b/src/openbench/runner/benchmark.py @@ -33,6 +33,7 @@ PipelineType.TRANSCRIPTION: TranscriptionSampleResult, PipelineType.ORCHESTRATION: TranscriptionSampleResult, PipelineType.STREAMING_TRANSCRIPTION: TranscriptionSampleResult, + PipelineType.SPEECH_GENERATION: TranscriptionSampleResult, } @@ -64,6 +65,7 @@ def __init__(self, config: BenchmarkConfig, pipelines: list[Pipeline]): PipelineType.TRANSCRIPTION: TranscriptionWandbLogger, PipelineType.ORCHESTRATION: TranscriptionWandbLogger, PipelineType.STREAMING_TRANSCRIPTION: TranscriptionWandbLogger, + PipelineType.SPEECH_GENERATION: TranscriptionWandbLogger, } def _get_metrics(self, pipeline: Pipeline) -> dict[str, BaseMetric]: diff --git a/src/openbench/types.py b/src/openbench/types.py index b4bbeaa..b82df22 100644 --- a/src/openbench/types.py +++ b/src/openbench/types.py @@ -12,6 +12,7 @@ class PipelineType(Enum): TRANSCRIPTION = "transcription" ORCHESTRATION = "orchestration" STREAMING_TRANSCRIPTION = "streaming_transcription" + SPEECH_GENERATION = "speech_generation" # All prediction classes that we output should conform to this From 7d3ab4d43ebbf4a1fd417e0bd9d6c11aebf1ad6b Mon Sep 17 00:00:00 2001 From: dberkin1 Date: Fri, 13 Feb 2026 20:58:09 +0300 Subject: [PATCH 02/15] refactor --- src/openbench/cli/commands/evaluate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/openbench/cli/commands/evaluate.py b/src/openbench/cli/commands/evaluate.py index c35eb01..d3f172c 100644 --- a/src/openbench/cli/commands/evaluate.py +++ b/src/openbench/cli/commands/evaluate.py @@ -220,7 +220,7 @@ def run_alias_mode( key, value = item.split("=", 1) pipeline_config_override[key] = value if verbose: - typer.echo(f"✅ Config override: {key}={value}") + typer.echo(f"Config override: {key}={value}") pipeline = PipelineRegistry.create_pipeline(pipeline_name, config=pipeline_config_override) From d41818130e97dc24f2ac206bcc8a5b737007a52a Mon Sep 17 00:00:00 2001 From: dberkin1 Date: Fri, 13 Feb 2026 21:05:08 +0300 Subject: [PATCH 03/15] reformatting --- src/openbench/cli/commands/evaluate.py | 5 +- .../dataset/dataset_speech_generation.py | 21 +-- .../pipeline/speech_generation/common.py | 25 +-- .../speech_generation_wkp.py | 151 ++++++------------ 4 files changed, 57 insertions(+), 145 deletions(-) diff --git a/src/openbench/cli/commands/evaluate.py b/src/openbench/cli/commands/evaluate.py index d3f172c..7470c20 100644 --- a/src/openbench/cli/commands/evaluate.py +++ b/src/openbench/cli/commands/evaluate.py @@ -213,10 +213,7 @@ def run_alias_mode( if pipeline_config: for item in pipeline_config: if "=" not in item: - raise typer.BadParameter( - f"Invalid --pipeline-config format: '{item}'. " - f"Expected key=value" - ) + raise typer.BadParameter(f"Invalid --pipeline-config format: '{item}'. Expected key=value") key, value = item.split("=", 1) pipeline_config_override[key] = value if verbose: diff --git a/src/openbench/dataset/dataset_speech_generation.py b/src/openbench/dataset/dataset_speech_generation.py index d9e2748..61d7acd 100644 --- a/src/openbench/dataset/dataset_speech_generation.py +++ b/src/openbench/dataset/dataset_speech_generation.py @@ -24,9 +24,7 @@ class SpeechGenerationRow(TypedDict): text: str -class SpeechGenerationSample( - BaseSample[Transcript, SpeechGenerationExtraInfo] -): +class SpeechGenerationSample(BaseSample[Transcript, SpeechGenerationExtraInfo]): """Sample for speech generation tasks. The reference Transcript is created from the text @@ -36,10 +34,7 @@ class SpeechGenerationSample( generated_audio_duration: float | None = Field( default=None, - description=( - "Duration (seconds) of the TTS-generated audio. " - "Set by the pipeline after generation." - ), + description=("Duration (seconds) of the TTS-generated audio. Set by the pipeline after generation."), ) def get_audio_duration(self) -> float: @@ -58,9 +53,7 @@ def text(self) -> str: return self.reference.get_transcript_string() -class SpeechGenerationDataset( - BaseDataset[SpeechGenerationSample] -): +class SpeechGenerationDataset(BaseDataset[SpeechGenerationSample]): """Dataset for speech generation pipelines. Expects column: 'text' (the prompt string). @@ -71,9 +64,7 @@ class SpeechGenerationDataset( _expected_columns = ["text"] _sample_class = SpeechGenerationSample - def _extract_audio_info( - self, row: dict - ) -> tuple[str, np.ndarray, int]: + def _extract_audio_info(self, row: dict) -> tuple[str, np.ndarray, int]: """Override to provide dummy audio info. Speech generation datasets don't have input audio. @@ -89,9 +80,7 @@ def _extract_audio_info( dummy_sample_rate = 16000 return audio_name, dummy_waveform, dummy_sample_rate - def prepare_sample( - self, row: SpeechGenerationRow - ) -> tuple[Transcript, SpeechGenerationExtraInfo]: + def prepare_sample(self, row: SpeechGenerationRow) -> tuple[Transcript, SpeechGenerationExtraInfo]: """Prepare reference from dataset row. Splits text prompt into words to create the diff --git a/src/openbench/pipeline/speech_generation/common.py b/src/openbench/pipeline/speech_generation/common.py index 96b924e..fd8b7b4 100644 --- a/src/openbench/pipeline/speech_generation/common.py +++ b/src/openbench/pipeline/speech_generation/common.py @@ -12,10 +12,7 @@ class SpeechGenerationConfig(PipelineConfig): cli_path: str = Field( ..., - description=( - "Path to the whisperkit-cli binary " - "(used for TTS generation)." - ), + description=("Path to the whisperkit-cli binary (used for TTS generation)."), ) # TTS parameters @@ -63,31 +60,19 @@ class SpeechGenerationConfig(PipelineConfig): # Transcription parameters transcription_cli_path: str | None = Field( default=None, - description=( - "Path to CLI for transcription. " - "Defaults to cli_path if not set." - ), + description=("Path to CLI for transcription. Defaults to cli_path if not set."), ) transcription_repo_id: str | None = Field( default=None, - description=( - "HuggingFace repo ID for transcription " - "model (e.g. argmaxinc/parakeetkit-pro)." - ), + description=("HuggingFace repo ID for transcription model (e.g. argmaxinc/parakeetkit-pro)."), ) transcription_model_variant: str | None = Field( default=None, - description=( - "Model variant folder within the repo " - "(e.g. nvidia_parakeet-v2_476MB)." - ), + description=("Model variant folder within the repo (e.g. nvidia_parakeet-v2_476MB)."), ) transcription_model_path: str | None = Field( default=None, - description=( - "Local path to ASR model dir. " - "Overrides repo_id/model_variant." - ), + description=("Local path to ASR model dir. Overrides repo_id/model_variant."), ) transcription_word_timestamps: bool = Field( default=True, diff --git a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py index d7191eb..6476d77 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py @@ -51,10 +51,7 @@ class SpeechGenerationInput(BaseModel): ) audio_name: str = Field( ..., - description=( - "Unique identifier for this sample " - "(used for temp file naming)." - ), + description=("Unique identifier for this sample (used for temp file naming)."), ) @@ -85,57 +82,44 @@ def build_pipeline( def generate_and_transcribe( input: SpeechGenerationInput, ) -> Transcript: - TEMP_TTS_AUDIO_DIR.mkdir( - parents=True, exist_ok=True - ) + TEMP_TTS_AUDIO_DIR.mkdir(parents=True, exist_ok=True) - audio_path = ( - TEMP_TTS_AUDIO_DIR - / f"{input.audio_name}.wav" - ) + audio_path = TEMP_TTS_AUDIO_DIR / f"{input.audio_name}.wav" # -- Step 1: Generate audio via TTS -- tts_cmd = [ config.cli_path, "tts", - "--text", input.text, - "--speaker", config.speaker, - "--language", config.language, - "--output-path", str(audio_path), - "--temperature", str(config.temperature), - "--top-k", str(config.top_k), + "--text", + input.text, + "--speaker", + config.speaker, + "--language", + config.language, + "--output-path", + str(audio_path), + "--temperature", + str(config.temperature), + "--top-k", + str(config.top_k), "--max-new-tokens", str(config.max_new_tokens), ] if config.seed is not None: - tts_cmd.extend( - ["--seed", str(config.seed)] - ) + tts_cmd.extend(["--seed", str(config.seed)]) if config.models_path is not None: - tts_cmd.extend( - ["--models-path", config.models_path] - ) + tts_cmd.extend(["--models-path", config.models_path]) if config.model_repo is not None: - tts_cmd.extend( - ["--model-repo", config.model_repo] - ) + tts_cmd.extend(["--model-repo", config.model_repo]) if config.version_dir is not None: - tts_cmd.extend( - ["--version-dir", config.version_dir] - ) + tts_cmd.extend(["--version-dir", config.version_dir]) if config.tokenizer is not None: - tts_cmd.extend( - ["--tokenizer", config.tokenizer] - ) + tts_cmd.extend(["--tokenizer", config.tokenizer]) - logger.debug( - f"Running TTS: {' '.join(tts_cmd)}" - ) + logger.debug(f"Running TTS: {' '.join(tts_cmd)}") - tts_result = subprocess.run( - tts_cmd, capture_output=True, text=True - ) + tts_result = subprocess.run(tts_cmd, capture_output=True, text=True) if tts_result.returncode != 0: raise RuntimeError( @@ -148,39 +132,27 @@ def generate_and_transcribe( ) if not audio_path.exists(): - raise RuntimeError( - "TTS completed but audio file " - f"not found at {audio_path}" - ) + raise RuntimeError(f"TTS completed but audio file not found at {audio_path}") - logger.info( - f"Generated TTS audio: {audio_path}" - ) + logger.info(f"Generated TTS audio: {audio_path}") # -- Step 2: Read audio duration before # transcription (engine may delete file) -- try: import soundfile as sf + info = sf.info(str(audio_path)) - pipeline_ref._last_generated_duration = ( - info.duration - ) + pipeline_ref._last_generated_duration = info.duration except Exception as e: - logger.warning( - f"WAV duration read failed: {e}" - ) - pipeline_ref._last_generated_duration = ( - None - ) + logger.warning(f"WAV duration read failed: {e}") + pipeline_ref._last_generated_duration = None # -- Step 3: Transcribe via WhisperKitPro -- engine_input = WhisperKitProInput( audio_path=audio_path, keep_audio=False, ) - engine_output = transcription_engine( - engine_input - ) + engine_output = transcription_engine(engine_input) # -- Step 4: Parse transcription report -- json_path = engine_output.json_report_path @@ -188,27 +160,21 @@ def generate_and_transcribe( with json_path.open("r") as f: data = json.load(f) all_words, all_starts, all_ends = ( - [], [], [], + [], + [], + [], ) for seg in data.get("segments", []): for w in seg.get("words", []): all_words.append(w["word"]) if "start" in w: - all_starts.append( - w["start"] - ) + all_starts.append(w["start"]) if "end" in w: all_ends.append(w["end"]) transcript = Transcript.from_words_info( words=all_words, - start=( - all_starts - if all_starts else None - ), - end=( - all_ends - if all_ends else None - ), + start=(all_starts if all_starts else None), + end=(all_ends if all_ends else None), ) # Clean up report files json_path.unlink(missing_ok=True) @@ -216,17 +182,9 @@ def generate_and_transcribe( if srt_path: srt_path.unlink(missing_ok=True) else: - raise RuntimeError( - "Transcription report not found " - f"at {json_path}" - ) + raise RuntimeError(f"Transcription report not found at {json_path}") - logger.info( - "Transcription: " - + transcript.get_transcript_string( - )[:100] - + "..." - ) + logger.info("Transcription: " + transcript.get_transcript_string()[:100] + "...") return transcript @@ -240,11 +198,10 @@ def _build_transcription_engine(self) -> WhisperKitPro: handles model download, caching, and CLI args. """ config = self.config - cli_path = ( - config.transcription_cli_path or config.cli_path - ) + cli_path = config.transcription_cli_path or config.cli_path import coremltools as ct + engine_config = WhisperKitProConfig( repo_id=config.transcription_repo_id, model_variant=config.transcription_model_variant, @@ -260,9 +217,7 @@ def _build_transcription_engine(self) -> WhisperKitPro: transcription_config=engine_config, ) - def __call__( - self, input_sample: BaseSample - ) -> PipelineOutput: + def __call__(self, input_sample: BaseSample) -> PipelineOutput: """Run pipeline and set generated audio duration. Overrides base __call__ to propagate the real @@ -282,35 +237,21 @@ def __call__( # Propagate generated audio duration to sample dur = self._last_generated_duration - logger.info( - f"Generated audio duration: {dur}s" - ) - if ( - isinstance(input_sample, SpeechGenerationSample) - and dur is not None - ): + logger.info(f"Generated audio duration: {dur}s") + if isinstance(input_sample, SpeechGenerationSample) and dur is not None: input_sample.generated_audio_duration = dur - logger.info( - "Set sample duration to " - f"{input_sample.generated_audio_duration}s" - ) + logger.info(f"Set sample duration to {input_sample.generated_audio_duration}s") return parsed_output - def parse_input( - self, input_sample: SpeechGenerationSample - ) -> SpeechGenerationInput: + def parse_input(self, input_sample: SpeechGenerationSample) -> SpeechGenerationInput: """Extract text prompt from the sample.""" - text = ( - input_sample.reference.get_transcript_string() - ) + text = input_sample.reference.get_transcript_string() return SpeechGenerationInput( text=text, audio_name=input_sample.audio_name, ) - def parse_output( - self, output: Transcript - ) -> SpeechGenerationOutput: + def parse_output(self, output: Transcript) -> SpeechGenerationOutput: """Wrap transcription into output.""" return SpeechGenerationOutput(prediction=output) From ef86575f157e0e8e5494d51a518b4cbcb23c20fd Mon Sep 17 00:00:00 2001 From: dberkin1 Date: Fri, 13 Feb 2026 21:12:03 +0300 Subject: [PATCH 04/15] reformat --- .../pipeline/speech_generation/speech_generation_wkp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py index 6476d77..9c93440 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py @@ -237,10 +237,10 @@ def __call__(self, input_sample: BaseSample) -> PipelineOutput: # Propagate generated audio duration to sample dur = self._last_generated_duration - logger.info(f"Generated audio duration: {dur}s") + logger.debug(f"Generated audio duration: {dur}s") if isinstance(input_sample, SpeechGenerationSample) and dur is not None: input_sample.generated_audio_duration = dur - logger.info(f"Set sample duration to {input_sample.generated_audio_duration}s") + logger.debug(f"Set sample duration to {input_sample.generated_audio_duration}s") return parsed_output From 4b7aca5fe1638ea01cf12cfa4a7c24b4f3b2caca Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Thu, 30 Apr 2026 16:17:05 -0300 Subject: [PATCH 05/15] Slim TTS evaluation: TTS-only pipeline, WER metric does ASR --- src/openbench/cli/command_utils.py | 43 +++ src/openbench/cli/commands/evaluate.py | 26 +- .../dataset/dataset_speech_generation.py | 49 +--- src/openbench/metric/__init__.py | 1 + src/openbench/metric/registry.py | 33 ++- .../metric/word_error_metrics/__init__.py | 1 + .../speech_generation_wer.py | 144 ++++++++++ .../word_error_metrics/word_error_metrics.py | 1 - src/openbench/pipeline/pipeline_aliases.py | 12 +- .../pipeline/speech_generation/common.py | 95 ------ .../speech_generation_wkp.py | 272 +++++++----------- src/openbench/pipeline_prediction.py | 44 +++ src/openbench/runner/benchmark.py | 20 +- src/openbench/runner/data_models.py | 13 +- src/openbench/runner/wandb_logger.py | 19 ++ tests/cli/test_pipeline_config_overrides.py | 126 ++++++++ 16 files changed, 561 insertions(+), 338 deletions(-) create mode 100644 src/openbench/metric/word_error_metrics/speech_generation_wer.py create mode 100644 tests/cli/test_pipeline_config_overrides.py diff --git a/src/openbench/cli/command_utils.py b/src/openbench/cli/command_utils.py index 564479a..82a4b61 100644 --- a/src/openbench/cli/command_utils.py +++ b/src/openbench/cli/command_utils.py @@ -16,6 +16,49 @@ def get_available_pipelines() -> list[str]: return PipelineRegistry.list_pipelines() +def parse_pipeline_config_overrides(items: list[str] | None) -> dict[str, str]: + """Parse a list of `key=value` strings into a config-override dict. + + Values are kept as raw strings. Pydantic performs the actual type + coercion when the pipeline config is built — so e.g. `seed=10`, + `temperature=0.9`, and `force_language=true` all work as expected + against a `SpeechGenerationConfig` (or any other Pydantic config), + because Pydantic's lax mode parses ints, floats, and the standard + boolean spellings ("true"/"false"/"yes"/"no"/"1"/"0", case-insensitive) + from strings. Quote arguments that contain spaces. + + Examples: + --pipeline-config speaker=serena + --pipeline-config seed=42 -pc temperature=0.7 + --pipeline-config force_language=true + + Args: + items: The raw values typer collected for `--pipeline-config`, + or `None` if the flag wasn't passed. + + Returns: + Dict mapping config keys to (string) values. Empty if `items` + is `None` or empty. + + Raises: + typer.BadParameter: If any item is missing the `=` separator, + or has an empty key. + """ + overrides: dict[str, str] = {} + if not items: + return overrides + + for item in items: + if "=" not in item: + raise typer.BadParameter(f"Invalid --pipeline-config format: '{item}'. Expected key=value (e.g. seed=42)") + key, value = item.split("=", 1) + key = key.strip() + if not key: + raise typer.BadParameter(f"Invalid --pipeline-config format: '{item}'. Empty key before '='.") + overrides[key] = value + return overrides + + def get_available_datasets() -> list[str]: """Get list of available dataset aliases.""" return DatasetRegistry.list_aliases() diff --git a/src/openbench/cli/commands/evaluate.py b/src/openbench/cli/commands/evaluate.py index 7470c20..7bde16d 100644 --- a/src/openbench/cli/commands/evaluate.py +++ b/src/openbench/cli/commands/evaluate.py @@ -24,6 +24,7 @@ get_datasets_help_text, get_metrics_help_text, get_pipelines_help_text, + parse_pipeline_config_overrides, validate_dataset_name, validate_pipeline_dataset_compatibility, validate_pipeline_metrics_compatibility, @@ -209,15 +210,13 @@ def run_alias_mode( if verbose: typer.echo("✅ Force language: enabled") - # Handle generic pipeline config overrides (key=value pairs) - if pipeline_config: - for item in pipeline_config: - if "=" not in item: - raise typer.BadParameter(f"Invalid --pipeline-config format: '{item}'. Expected key=value") - key, value = item.split("=", 1) - pipeline_config_override[key] = value - if verbose: - typer.echo(f"Config override: {key}={value}") + # Handle generic pipeline config overrides (key=value pairs). + # Values are kept as strings; the pipeline's Pydantic config + # coerces them to int/float/bool/etc when instantiated. + for key, value in parse_pipeline_config_overrides(pipeline_config).items(): + pipeline_config_override[key] = value + if verbose: + typer.echo(f"Config override: {key}={value}") pipeline = PipelineRegistry.create_pipeline(pipeline_name, config=pipeline_config_override) @@ -360,7 +359,14 @@ def evaluate( None, "--pipeline-config", "-pc", - help="Override pipeline config values as key=value pairs (e.g. --pipeline-config speaker=serena)", + help=( + "Override one or more pipeline config fields as key=value pairs. " + "The value is parsed as a string; Pydantic coerces it to the field's " + "declared type when the pipeline is instantiated, so ints/floats/bools " + "all just work. Repeat the flag for multiple overrides. Examples: " + "`-pc speaker=serena`, `-pc seed=42 -pc temperature=0.7`, " + "`-pc force_language=true`." + ), ), verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output"), ) -> None: diff --git a/src/openbench/dataset/dataset_speech_generation.py b/src/openbench/dataset/dataset_speech_generation.py index 61d7acd..f96110d 100644 --- a/src/openbench/dataset/dataset_speech_generation.py +++ b/src/openbench/dataset/dataset_speech_generation.py @@ -2,7 +2,6 @@ # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. import numpy as np -from pydantic import Field from typing_extensions import TypedDict from ..pipeline_prediction import Transcript @@ -25,28 +24,14 @@ class SpeechGenerationRow(TypedDict): class SpeechGenerationSample(BaseSample[Transcript, SpeechGenerationExtraInfo]): - """Sample for speech generation tasks. + """Sample for speech-generation tasks. - The reference Transcript is created from the text - prompt. The pipeline generates audio from this text - and transcribes it to compute WER against reference. + The reference `Transcript` is constructed from the prompt text. The + pipeline synthesizes audio from this prompt and returns a + `GeneratedAudio` prediction; the WER metric transcribes that audio + and compares against this reference. """ - generated_audio_duration: float | None = Field( - default=None, - description=("Duration (seconds) of the TTS-generated audio. Set by the pipeline after generation."), - ) - - def get_audio_duration(self) -> float: - """Return generated audio duration if available. - - Falls back to the dummy waveform calculation - when the pipeline hasn't set the real duration yet. - """ - if self.generated_audio_duration is not None: - return self.generated_audio_duration - return super().get_audio_duration() - @property def text(self) -> str: """The original text prompt.""" @@ -54,24 +39,20 @@ def text(self) -> str: class SpeechGenerationDataset(BaseDataset[SpeechGenerationSample]): - """Dataset for speech generation pipelines. + """Dataset for speech-generation pipelines. - Expects column: 'text' (the prompt string). - No audio column is required since audio is generated - by the pipeline itself. + Expects column: 'text' (the prompt string). No audio column is + required — audio is produced by the pipeline. A dummy waveform is + supplied to satisfy the base sample structure but is ignored + everywhere downstream; the runner reads the real generated-audio + duration off the pipeline output. """ _expected_columns = ["text"] _sample_class = SpeechGenerationSample def _extract_audio_info(self, row: dict) -> tuple[str, np.ndarray, int]: - """Override to provide dummy audio info. - - Speech generation datasets don't have input audio. - We provide a placeholder waveform so the framework - sample structure is satisfied. The pipeline ignores - the waveform entirely. - """ + """Provide a placeholder waveform; speech-generation has no input audio.""" audio_name = f"sample_{row['idx']}" # Use audio_name from the row if available if "audio_name" in row and row["audio_name"]: @@ -81,11 +62,7 @@ def _extract_audio_info(self, row: dict) -> tuple[str, np.ndarray, int]: return audio_name, dummy_waveform, dummy_sample_rate def prepare_sample(self, row: SpeechGenerationRow) -> tuple[Transcript, SpeechGenerationExtraInfo]: - """Prepare reference from dataset row. - - Splits text prompt into words to create the - reference Transcript. - """ + """Build the reference transcript from the prompt text.""" text = row["text"] words = text.split() reference = Transcript.from_words_info( diff --git a/src/openbench/metric/__init__.py b/src/openbench/metric/__init__.py index 284d03e..ac816f6 100644 --- a/src/openbench/metric/__init__.py +++ b/src/openbench/metric/__init__.py @@ -24,6 +24,7 @@ ) from .word_error_metrics import ( ConcatenatedMinimumPermutationWER, + SpeechGenerationWordErrorRate, WordDiarizationErrorRate, WordErrorRate, ) diff --git a/src/openbench/metric/registry.py b/src/openbench/metric/registry.py index 99e991b..dcf3c2d 100644 --- a/src/openbench/metric/registry.py +++ b/src/openbench/metric/registry.py @@ -28,10 +28,14 @@ class MetricRegistry: """Registry for metrics by pipeline type. This registry allows registering and retrieving metrics based on pipeline type. - Each metric can be supported by multiple pipeline types. + The same MetricOption (e.g. WER) can map to different metric classes for + different pipeline types — for example a SPEECH_GENERATION pipeline can register + a WER implementation that first transcribes generated audio, while + TRANSCRIPTION uses the standard transcript-vs-transcript implementation. """ - _metrics: ClassVar[dict[MetricOptions, dict[str, tuple[PipelineType, ...] | Type[BaseMetric]]]] = {} + # Pipeline type -> Metric option -> Metric class + _metrics: ClassVar[dict[PipelineType, dict[MetricOptions, Type[BaseMetric]]]] = {} @classmethod def register( @@ -51,10 +55,8 @@ def register( if isinstance(pipeline_types, PipelineType): pipeline_types = (pipeline_types,) - cls._metrics[metric_option] = { - "supported_pipelines": pipeline_types, - "metric_class": metric_class, - } + for pipeline_type in pipeline_types: + cls._metrics.setdefault(pipeline_type, {})[metric_option] = metric_class @classmethod def register_metric( @@ -97,10 +99,11 @@ def decorator(metric_class: Type[T]) -> Type[T]: return decorator @classmethod - def get_metric(cls, metric_option: MetricOptions, **kwargs) -> BaseMetric: + def get_metric(cls, pipeline_type: PipelineType, metric_option: MetricOptions, **kwargs) -> BaseMetric: """Get a metric instance for a specific pipeline type and metric option. Args: + pipeline_type: The pipeline type the metric is being requested for metric_option: The metric option to get **kwargs: Additional arguments to pass to the metric constructor @@ -108,13 +111,13 @@ def get_metric(cls, metric_option: MetricOptions, **kwargs) -> BaseMetric: An instance of the requested metric Raises: - KeyError: If the metric is not registered or not supported for the given pipeline type + KeyError: If the metric is not registered for the given pipeline type """ - if metric_option not in cls._metrics: - raise KeyError(f"Metric {metric_option} not registered") + pipeline_metrics = cls._metrics.get(pipeline_type) + if pipeline_metrics is None or metric_option not in pipeline_metrics: + raise KeyError(f"Metric {metric_option} not registered for pipeline type {pipeline_type}") - metric_info = cls._metrics[metric_option] - return metric_info["metric_class"](**kwargs) + return pipeline_metrics[metric_option](**kwargs) @classmethod def get_available_metrics(cls, pipeline_type: PipelineType) -> list[MetricOptions]: @@ -126,11 +129,7 @@ def get_available_metrics(cls, pipeline_type: PipelineType) -> list[MetricOption Returns: List of available metric options """ - return [ - metric_option - for metric_option, metric_info in cls._metrics.items() - if pipeline_type in metric_info["supported_pipelines"] - ] + return list(cls._metrics.get(pipeline_type, {}).keys()) # Register all existing and interesting metrics from pyannote.metrics diff --git a/src/openbench/metric/word_error_metrics/__init__.py b/src/openbench/metric/word_error_metrics/__init__.py index 305aace..6528d49 100644 --- a/src/openbench/metric/word_error_metrics/__init__.py +++ b/src/openbench/metric/word_error_metrics/__init__.py @@ -2,6 +2,7 @@ # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. from .english_abbreviations import ABBR +from .speech_generation_wer import SpeechGenerationWordErrorRate from .text_normalizer import EnglishTextNormalizer from .word_error_metrics import ( ConcatenatedMinimumPermutationWER, diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py new file mode 100644 index 0000000..4fdd7a2 --- /dev/null +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -0,0 +1,144 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +"""Word Error Rate for speech-generation pipelines. + +The metric receives a `GeneratedAudio` hypothesis (path + duration), runs +ASR on the audio to obtain a `Transcript`, and then delegates to the +standard `WordErrorRate.compute_components` against the reference. This +keeps the TTS pipeline free of ASR concerns — including from its timing +budget — while still letting WER score generated audio. + +Transcription is performed by a caller-supplied callable +`transcribe_fn(audio_path: str) -> Transcript`. When not supplied a default +WhisperKitPro-backed implementation is built lazily (parakeet-v2 ASR), so +users who only need this metric with the project's default ASR don't have +to wire anything up. A future PR will plumb metric-level configuration +through the CLI; for now constructor injection is the supported swap +point. +""" + +import json +import os +from pathlib import Path +from typing import Callable + +import coremltools as ct +from argmaxtools.utils import get_logger + +from ...engine.whisperkitpro_engine import WhisperKitPro, WhisperKitProConfig, WhisperKitProInput +from ...pipeline_prediction import GeneratedAudio, Transcript +from ...types import PipelineType +from ..metric import MetricOptions +from ..registry import MetricRegistry +from .word_error_metrics import WordErrorRate + + +logger = get_logger(__name__) + + +def _whisperkitpro_transcribe_factory( + cli_path: str | None = None, + repo_id: str = "argmaxinc/parakeetkit-pro", + model_variant: str = "nvidia_parakeet-v2_476MB", +) -> Callable[[str], Transcript]: + """Build a default `transcribe_fn` backed by the WhisperKitPro engine. + + The engine (and underlying model) is constructed once and reused + across every call to the returned function. + """ + cli_path = cli_path or os.getenv("WHISPERKITPRO_CLI_PATH") + if not cli_path: + raise RuntimeError( + "SpeechGenerationWordErrorRate default transcription requires " + "WHISPERKITPRO_CLI_PATH (or an explicit cli_path). Pass " + "transcribe_fn= to the metric to use a different backend." + ) + + engine_config = WhisperKitProConfig( + repo_id=repo_id, + model_variant=model_variant, + word_timestamps=True, + chunking_strategy="vad", + audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, + text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, + ) + engine = WhisperKitPro(cli_path=cli_path, transcription_config=engine_config) + + def transcribe(audio_path: str) -> Transcript: + engine_output = engine(WhisperKitProInput(audio_path=Path(audio_path), keep_audio=False)) + + json_path = engine_output.json_report_path + try: + if not json_path.exists(): + raise RuntimeError(f"Transcription report not found at {json_path}") + with json_path.open("r") as f: + data = json.load(f) + + words, starts, ends = [], [], [] + for seg in data.get("segments", []): + for w in seg.get("words", []): + words.append(w["word"]) + if "start" in w: + starts.append(w["start"]) + if "end" in w: + ends.append(w["end"]) + + return Transcript.from_words_info( + words=words, + start=starts if starts else None, + end=ends if ends else None, + ) + finally: + json_path.unlink(missing_ok=True) + srt_path = engine_output.srt_report_path + if srt_path is not None: + Path(srt_path).unlink(missing_ok=True) + + return transcribe + + +@MetricRegistry.register_metric(PipelineType.SPEECH_GENERATION, MetricOptions.WER) +class SpeechGenerationWordErrorRate(WordErrorRate): + """WER metric for speech-generation pipelines. + + Args: + transcribe_fn: Callable taking an audio file path and returning a + `Transcript`. If `None`, a default WhisperKitPro+parakeet-v2 + transcriber is built lazily on first use. + **kwargs: Forwarded to `WordErrorRate.__init__` + (e.g. `use_text_normalizer`, `english_spelling_mapping`). + """ + + def __init__( + self, + transcribe_fn: Callable[[str], Transcript] | None = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self._explicit_transcribe = transcribe_fn + self._default_transcribe: Callable[[str], Transcript] | None = None + + def _get_transcribe(self) -> Callable[[str], Transcript]: + if self._explicit_transcribe is not None: + return self._explicit_transcribe + if self._default_transcribe is None: + logger.info("Building default WhisperKitPro transcriber for speech-generation WER") + self._default_transcribe = _whisperkitpro_transcribe_factory() + return self._default_transcribe + + def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, **kwargs) -> dict[str, int]: + """Transcribe the generated audio, then delegate to WER. + + Args: + reference: Reference transcript built from the original prompt. + hypothesis: Generated audio (path + duration) produced by a TTS pipeline. + """ + if not isinstance(hypothesis, GeneratedAudio): + raise TypeError( + f"SpeechGenerationWordErrorRate expected hypothesis of type GeneratedAudio, " + f"got {type(hypothesis).__name__}" + ) + hypothesis_transcript = self._get_transcribe()(hypothesis.audio_path) + logger.info("TTS WER hypothesis transcript: " + hypothesis_transcript.get_transcript_string()[:120] + "...") + return super().compute_components(reference, hypothesis_transcript, **kwargs) diff --git a/src/openbench/metric/word_error_metrics/word_error_metrics.py b/src/openbench/metric/word_error_metrics/word_error_metrics.py index 24655d5..4cef2a1 100644 --- a/src/openbench/metric/word_error_metrics/word_error_metrics.py +++ b/src/openbench/metric/word_error_metrics/word_error_metrics.py @@ -223,7 +223,6 @@ def compute_metric(self, detail: Details) -> float: PipelineType.TRANSCRIPTION, PipelineType.ORCHESTRATION, PipelineType.STREAMING_TRANSCRIPTION, - PipelineType.SPEECH_GENERATION, ), MetricOptions.WER, ) diff --git a/src/openbench/pipeline/pipeline_aliases.py b/src/openbench/pipeline/pipeline_aliases.py index 2ce0012..2ed3dd1 100644 --- a/src/openbench/pipeline/pipeline_aliases.py +++ b/src/openbench/pipeline/pipeline_aliases.py @@ -673,13 +673,13 @@ def register_pipeline_aliases() -> None: "temperature": 0.9, "top_k": 50, "max_new_tokens": 245, - "transcription_cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"), - "transcription_repo_id": "argmaxinc/parakeetkit-pro", - "transcription_model_variant": "nvidia_parakeet-v2_476MB", }, - description="WhisperKit speech generation pipeline. Generates audio from text prompts using whisperkit-cli TTS, " - "then transcribes the generated audio to compute WER against the original prompt. " - "Requires `WHISPERKIT_CLI_PATH` env var pointing to the whisperkit-cli binary.", + description=( + "WhisperKit speech-generation pipeline. Synthesizes audio from text prompts via whisperkit-cli TTS. " + "WER is computed by `SpeechGenerationWordErrorRate`, which transcribes the generated audio with " + "WhisperKitPro / parakeet-v2 by default and compares against the original prompt. " + "Requires `WHISPERKIT_CLI_PATH` for TTS; the default WER metric also requires `WHISPERKITPRO_CLI_PATH`." + ), ) ################# STREAMING TRANSCRIPTION PIPELINES ################# diff --git a/src/openbench/pipeline/speech_generation/common.py b/src/openbench/pipeline/speech_generation/common.py index fd8b7b4..e69de29 100644 --- a/src/openbench/pipeline/speech_generation/common.py +++ b/src/openbench/pipeline/speech_generation/common.py @@ -1,95 +0,0 @@ -# For licensing see accompanying LICENSE.md file. -# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. - -from pydantic import Field - -from ...pipeline_prediction import Transcript -from ..base import PipelineConfig, PipelineOutput - - -class SpeechGenerationConfig(PipelineConfig): - """Base config for speech generation pipelines.""" - - cli_path: str = Field( - ..., - description=("Path to the whisperkit-cli binary (used for TTS generation)."), - ) - - # TTS parameters - speaker: str = Field( - default="aiden", - description="Speaker voice for TTS generation.", - ) - language: str = Field( - default="english", - description="Language for TTS generation.", - ) - seed: int | None = Field( - default=None, - description="Random seed for reproducible output.", - ) - temperature: float = Field( - default=0.9, - description="Sampling temperature for TTS.", - ) - top_k: int = Field( - default=50, - description="Top-k sampling for TTS.", - ) - max_new_tokens: int = Field( - default=245, - description="Max RVQ frames to generate.", - ) - models_path: str | None = Field( - default=None, - description="Local model directory for TTS.", - ) - model_repo: str | None = Field( - default=None, - description="HF repo for TTS model download.", - ) - version_dir: str | None = Field( - default=None, - description="TTS model version directory.", - ) - tokenizer: str | None = Field( - default=None, - description="HF tokenizer repo or local path.", - ) - - # Transcription parameters - transcription_cli_path: str | None = Field( - default=None, - description=("Path to CLI for transcription. Defaults to cli_path if not set."), - ) - transcription_repo_id: str | None = Field( - default=None, - description=("HuggingFace repo ID for transcription model (e.g. argmaxinc/parakeetkit-pro)."), - ) - transcription_model_variant: str | None = Field( - default=None, - description=("Model variant folder within the repo (e.g. nvidia_parakeet-v2_476MB)."), - ) - transcription_model_path: str | None = Field( - default=None, - description=("Local path to ASR model dir. Overrides repo_id/model_variant."), - ) - transcription_word_timestamps: bool = Field( - default=True, - description="Include word timestamps.", - ) - transcription_chunking_strategy: str = Field( - default="vad", - description="Chunking strategy (none or vad).", - ) - - -class SpeechGenerationOutput(PipelineOutput[Transcript]): - """Output for speech generation pipelines. - - The prediction is a Transcript of the generated audio - (obtained by transcribing the TTS output). WER is - computed against the original text prompt. - """ - - pass diff --git a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py index 9c93440..49d2678 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py @@ -1,40 +1,32 @@ # For licensing see accompanying LICENSE.md file. # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. -""" -Speech generation pipeline using WhisperKit CLI. +"""Speech-generation pipeline using the WhisperKit CLI. -Generates TTS audio from text prompts, then transcribes -the generated audio back to text for WER evaluation -against the original prompt. +The pipeline shells out to `whisperkit-cli tts` to synthesize a WAV from +a text prompt and returns a `GeneratedAudio` prediction containing the +file path and the measured duration. Transcription / WER scoring is +performed by the `SpeechGenerationWordErrorRate` metric, not here, so +the pipeline's reported `prediction_time` reflects TTS only. """ -import json import subprocess -import time from pathlib import Path from typing import Callable +import librosa from argmaxtools.utils import get_logger from pydantic import BaseModel, Field -from ...dataset.dataset_base import BaseSample -from ...dataset.dataset_speech_generation import ( - SpeechGenerationSample, -) -from ...engine.whisperkitpro_engine import ( - WhisperKitPro, - WhisperKitProConfig, - WhisperKitProInput, -) -from ...pipeline_prediction import Transcript +from ...dataset.dataset_speech_generation import SpeechGenerationSample +from ...pipeline_prediction import GeneratedAudio from ..base import ( Pipeline, + PipelineConfig, PipelineOutput, PipelineType, register_pipeline, ) -from .common import SpeechGenerationConfig, SpeechGenerationOutput logger = get_logger(__name__) @@ -42,6 +34,60 @@ TEMP_TTS_AUDIO_DIR = Path("./temp_tts_audio") +class WhisperKitSpeechGenerationConfig(PipelineConfig): + """Config for the WhisperKit speech-generation pipeline. + + All fields are specific to whisperkit-cli; we don't currently share + anything across TTS providers, so this config lives next to the + pipeline rather than under a common base. + """ + + cli_path: str = Field( + ..., + description="Path to the whisperkit-cli binary (used for TTS generation).", + ) + speaker: str = Field( + default="aiden", + description="Speaker voice for TTS generation.", + ) + language: str = Field( + default="english", + description="Language for TTS generation.", + ) + seed: int | None = Field( + default=None, + description="Random seed for reproducible output.", + ) + temperature: float = Field( + default=0.9, + description="Sampling temperature for TTS.", + ) + top_k: int = Field( + default=50, + description="Top-k sampling for TTS.", + ) + max_new_tokens: int = Field( + default=245, + description="Max RVQ frames to generate.", + ) + models_path: str | None = Field( + default=None, + description="Local model directory for TTS.", + ) + model_repo: str | None = Field( + default=None, + description="HF repo for TTS model download.", + ) + version_dir: str | None = Field( + default=None, + description="TTS model version directory.", + ) + tokenizer: str | None = Field( + default=None, + description="HF tokenizer repo or local path.", + ) + + class SpeechGenerationInput(BaseModel): """Input for the speech generation pipeline.""" @@ -51,42 +97,37 @@ class SpeechGenerationInput(BaseModel): ) audio_name: str = Field( ..., - description=("Unique identifier for this sample (used for temp file naming)."), + description="Unique identifier for this sample (used for temp file naming).", ) @register_pipeline class WhisperKitSpeechGenerationPipeline(Pipeline): - """Speech generation pipeline using WhisperKit CLI. + """Speech-generation pipeline using the WhisperKit CLI. + + For each sample, the pipeline: - This pipeline: - 1. Generates audio from text via whisperkit-cli tts - 2. Transcribes audio via WhisperKitPro engine - 3. Returns transcription as Transcript for WER eval - 4. Cleans up temporary audio and report files + 1. Builds a `whisperkit-cli tts` invocation from the config. + 2. Synthesizes the WAV to a temp directory under cwd. + 3. Measures the duration via librosa. + 4. Returns a `GeneratedAudio` prediction (path + duration). + + The generated WAV is left in place so the WER metric can transcribe + it and the wandb logger can copy it into the predictions artifact. + On TTS failure the partial WAV (if any) is removed so the temp dir + doesn't accumulate stale files across retries. """ - _config_class = SpeechGenerationConfig + _config_class = WhisperKitSpeechGenerationConfig pipeline_type = PipelineType.SPEECH_GENERATION - def build_pipeline( - self, - ) -> Callable[[SpeechGenerationInput], Transcript]: + def build_pipeline(self) -> Callable[[SpeechGenerationInput], GeneratedAudio]: config = self.config - pipeline_ref = self - - # Build the WhisperKitPro engine for transcription - # (downloads model once, reuses for all samples) - transcription_engine = self._build_transcription_engine() - def generate_and_transcribe( - input: SpeechGenerationInput, - ) -> Transcript: + def generate(input: SpeechGenerationInput) -> GeneratedAudio: TEMP_TTS_AUDIO_DIR.mkdir(parents=True, exist_ok=True) - audio_path = TEMP_TTS_AUDIO_DIR / f"{input.audio_name}.wav" - # -- Step 1: Generate audio via TTS -- tts_cmd = [ config.cli_path, "tts", @@ -105,7 +146,6 @@ def generate_and_transcribe( "--max-new-tokens", str(config.max_new_tokens), ] - if config.seed is not None: tts_cmd.extend(["--seed", str(config.seed)]) if config.models_path is not None: @@ -119,139 +159,41 @@ def generate_and_transcribe( logger.debug(f"Running TTS: {' '.join(tts_cmd)}") - tts_result = subprocess.run(tts_cmd, capture_output=True, text=True) - - if tts_result.returncode != 0: - raise RuntimeError( - "whisperkit-cli tts failed " - f"(exit {tts_result.returncode}):\n" - f" stdout: " - f"{tts_result.stdout[:500]}\n" - f" stderr: " - f"{tts_result.stderr[:500]}" - ) - - if not audio_path.exists(): - raise RuntimeError(f"TTS completed but audio file not found at {audio_path}") - - logger.info(f"Generated TTS audio: {audio_path}") - - # -- Step 2: Read audio duration before - # transcription (engine may delete file) -- try: - import soundfile as sf - - info = sf.info(str(audio_path)) - pipeline_ref._last_generated_duration = info.duration - except Exception as e: - logger.warning(f"WAV duration read failed: {e}") - pipeline_ref._last_generated_duration = None - - # -- Step 3: Transcribe via WhisperKitPro -- - engine_input = WhisperKitProInput( - audio_path=audio_path, - keep_audio=False, - ) - engine_output = transcription_engine(engine_input) - - # -- Step 4: Parse transcription report -- - json_path = engine_output.json_report_path - if json_path.exists(): - with json_path.open("r") as f: - data = json.load(f) - all_words, all_starts, all_ends = ( - [], - [], - [], + tts_result = subprocess.run(tts_cmd, capture_output=True, text=True) + if tts_result.returncode != 0: + raise RuntimeError( + "whisperkit-cli tts failed " + f"(exit {tts_result.returncode}):\n" + f" stdout: {tts_result.stdout[:500]}\n" + f" stderr: {tts_result.stderr[:500]}" + ) + if not audio_path.exists(): + raise RuntimeError(f"TTS completed but audio file not found at {audio_path}") + + duration = float(librosa.get_duration(path=str(audio_path))) + logger.info(f"Generated TTS audio: {audio_path} ({duration:.2f}s)") + + return GeneratedAudio( + audio_path=str(audio_path), + duration=duration, ) - for seg in data.get("segments", []): - for w in seg.get("words", []): - all_words.append(w["word"]) - if "start" in w: - all_starts.append(w["start"]) - if "end" in w: - all_ends.append(w["end"]) - transcript = Transcript.from_words_info( - words=all_words, - start=(all_starts if all_starts else None), - end=(all_ends if all_ends else None), - ) - # Clean up report files - json_path.unlink(missing_ok=True) - srt_path = engine_output.srt_report_path - if srt_path: - srt_path.unlink(missing_ok=True) - else: - raise RuntimeError(f"Transcription report not found at {json_path}") - - logger.info("Transcription: " + transcript.get_transcript_string()[:100] + "...") - - return transcript - - return generate_and_transcribe - - def _build_transcription_engine(self) -> WhisperKitPro: - """Create WhisperKitPro engine for transcription. - - Uses the same engine as the dedicated - WhisperKitPro transcription pipelines, which - handles model download, caching, and CLI args. - """ - config = self.config - cli_path = config.transcription_cli_path or config.cli_path - - import coremltools as ct - - engine_config = WhisperKitProConfig( - repo_id=config.transcription_repo_id, - model_variant=config.transcription_model_variant, - model_dir=config.transcription_model_path, - word_timestamps=config.transcription_word_timestamps, - chunking_strategy=config.transcription_chunking_strategy, - audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, - text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, - ) - - return WhisperKitPro( - cli_path=cli_path, - transcription_config=engine_config, - ) - - def __call__(self, input_sample: BaseSample) -> PipelineOutput: - """Run pipeline and set generated audio duration. - - Overrides base __call__ to propagate the real - TTS audio duration back onto the sample so the - runner reports accurate audio_duration and - speed_factor values. - """ - self._last_generated_duration: float | None = None - parsed_input = self.parse_input(input_sample) - start_time = time.perf_counter() - output = self.pipeline(parsed_input) - end_time = time.perf_counter() - prediction_time = end_time - start_time - parsed_output = self.parse_output(output) - if parsed_output.prediction_time is None: - parsed_output.prediction_time = prediction_time - - # Propagate generated audio duration to sample - dur = self._last_generated_duration - logger.debug(f"Generated audio duration: {dur}s") - if isinstance(input_sample, SpeechGenerationSample) and dur is not None: - input_sample.generated_audio_duration = dur - logger.debug(f"Set sample duration to {input_sample.generated_audio_duration}s") + except Exception: + # On any failure during synthesis or duration probing, + # clean up the partial WAV so the temp dir doesn't grow. + audio_path.unlink(missing_ok=True) + raise - return parsed_output + return generate def parse_input(self, input_sample: SpeechGenerationSample) -> SpeechGenerationInput: - """Extract text prompt from the sample.""" + """Extract the text prompt and a stable identifier from the sample.""" text = input_sample.reference.get_transcript_string() return SpeechGenerationInput( text=text, audio_name=input_sample.audio_name, ) - def parse_output(self, output: Transcript) -> SpeechGenerationOutput: - """Wrap transcription into output.""" - return SpeechGenerationOutput(prediction=output) + def parse_output(self, output: GeneratedAudio) -> PipelineOutput[GeneratedAudio]: + """Wrap the generated audio in a `PipelineOutput`.""" + return PipelineOutput[GeneratedAudio](prediction=output) diff --git a/src/openbench/pipeline_prediction.py b/src/openbench/pipeline_prediction.py index 030d6d5..d95f9e6 100644 --- a/src/openbench/pipeline_prediction.py +++ b/src/openbench/pipeline_prediction.py @@ -3,7 +3,9 @@ import json import os +import shutil from collections import defaultdict +from pathlib import Path import matplotlib.pyplot as plt import numpy as np @@ -237,6 +239,48 @@ def to_annotation_file(self, output_dir: str, filename: str) -> str: return path +# Speech Generation Prediction +class GeneratedAudio(BaseModel): + """Prediction produced by speech-generation pipelines. + + Wraps the generated audio file path together with measured duration so + that downstream metrics (which transcribe the audio) and the runner + (which reports speed factor) can both consume it without reading the + waveform twice. + """ + + audio_path: str = Field( + ..., + description="Path to the generated audio file on disk (typically a WAV).", + ) + duration: float = Field( + ..., + description="Duration of the generated audio in seconds.", + ) + sample_rate: int | None = Field( + None, + description="Sample rate of the generated audio in Hz, if known.", + ) + + def to_annotation_file(self, output_dir: str, filename: str) -> str: + """Persist the generated audio under output_dir as `{filename}.wav`. + + The original `audio_path` is left intact (often a temp file managed + by the pipeline / metric); we copy rather than move so cleanup + responsibilities stay with the producer. + """ + src = Path(self.audio_path) + suffix = src.suffix or ".wav" + dst = Path(output_dir) / f"{filename}{suffix}" + if not src.exists(): + raise FileNotFoundError( + f"Cannot serialize GeneratedAudio: source {src} no longer exists " + "(was it deleted by an upstream cleanup step before serialization?)" + ) + shutil.copyfile(src, dst) + return str(dst) + + # NOTE: StreamingTranscript is used only as output of pipelines. The reference for streaming transcript is of type Transcript. class StreamingTranscript(BaseModel): transcript: str = Field(..., description="The final transcript") diff --git a/src/openbench/runner/benchmark.py b/src/openbench/runner/benchmark.py index b3f9e78..2595be9 100644 --- a/src/openbench/runner/benchmark.py +++ b/src/openbench/runner/benchmark.py @@ -19,11 +19,12 @@ BenchmarkResult, DiarizationSampleResult, GlobalResult, + SpeechGenerationSampleResult, TaskResult, TranscriptionSampleResult, ) from .utils import change_directory, get_global_results -from .wandb_logger import DiarizationWandbLogger, TranscriptionWandbLogger +from .wandb_logger import DiarizationWandbLogger, SpeechGenerationWandbLogger, TranscriptionWandbLogger logger = get_logger(__name__) @@ -33,12 +34,12 @@ PipelineType.TRANSCRIPTION: TranscriptionSampleResult, PipelineType.ORCHESTRATION: TranscriptionSampleResult, PipelineType.STREAMING_TRANSCRIPTION: TranscriptionSampleResult, - PipelineType.SPEECH_GENERATION: TranscriptionSampleResult, + PipelineType.SPEECH_GENERATION: SpeechGenerationSampleResult, } class ProcessingResult(NamedTuple): - sample_result: DiarizationSampleResult | TranscriptionSampleResult + sample_result: DiarizationSampleResult | TranscriptionSampleResult | SpeechGenerationSampleResult task_results: list[TaskResult] sample_id: int metrics_string: str @@ -65,7 +66,7 @@ def __init__(self, config: BenchmarkConfig, pipelines: list[Pipeline]): PipelineType.TRANSCRIPTION: TranscriptionWandbLogger, PipelineType.ORCHESTRATION: TranscriptionWandbLogger, PipelineType.STREAMING_TRANSCRIPTION: TranscriptionWandbLogger, - PipelineType.SPEECH_GENERATION: TranscriptionWandbLogger, + PipelineType.SPEECH_GENERATION: SpeechGenerationWandbLogger, } def _get_metrics(self, pipeline: Pipeline) -> dict[str, BaseMetric]: @@ -74,7 +75,7 @@ def _get_metrics(self, pipeline: Pipeline) -> dict[str, BaseMetric]: for metric_name, kwargs in self.config.metrics.items(): if metric_name not in available_metrics: continue - metrics_dict[metric_name] = MetricRegistry.get_metric(metric_name, **kwargs) + metrics_dict[metric_name] = MetricRegistry.get_metric(pipeline.pipeline_type, metric_name, **kwargs) return metrics_dict def _process_single_sample_wrapper(self, args): @@ -101,7 +102,14 @@ def _process_single_sample( f"from dataset {dataset_name} with pipeline {pipeline.__class__.__name__}: {e}" ) raise RuntimeError(f"Pipeline execution failed on sample {sample_id} ({sample.audio_name}): {e}") from e - audio_duration = sample.get_audio_duration() + + # For speech-generation pipelines the sample has no real input audio + # (only a placeholder waveform), so duration is read off the prediction + # produced by the TTS step. + if pipeline.pipeline_type == PipelineType.SPEECH_GENERATION: + audio_duration = output.prediction.duration + else: + audio_duration = sample.get_audio_duration() prediction_time = output.prediction_time # Create sample result diff --git a/src/openbench/runner/data_models.py b/src/openbench/runner/data_models.py index 5e4ab28..9b5e1d8 100644 --- a/src/openbench/runner/data_models.py +++ b/src/openbench/runner/data_models.py @@ -6,7 +6,7 @@ import numpy as np from pydantic import BaseModel, Field -from ..pipeline_prediction import DiarizationAnnotation, StreamingTranscript, Transcript +from ..pipeline_prediction import DiarizationAnnotation, GeneratedAudio, StreamingTranscript, Transcript from ..types import PredictionProtocol @@ -68,6 +68,15 @@ class TranscriptionSampleResult(BaseSampleResult[Union[Transcript, StreamingTran """ +class SpeechGenerationSampleResult(BaseSampleResult[GeneratedAudio]): + """The speech-generation result for a given sample of a dataset. + + The prediction is the generated audio (path + duration). The + transcription used to compute WER is internal to the metric and + surfaces only via TaskResult.detailed_result, not on this row. + """ + + class TaskResult(BaseModel): """The evaluation result for a given task on a given sample of a dataset""" @@ -101,7 +110,7 @@ class GlobalResult(BaseModel): class BenchmarkResult(BaseModel): - sample_results: list[DiarizationSampleResult | TranscriptionSampleResult] = Field( + sample_results: list[DiarizationSampleResult | TranscriptionSampleResult | SpeechGenerationSampleResult] = Field( ..., description="The results of the samples" ) task_results: list[TaskResult] = Field(..., description="The results of the tasks") diff --git a/src/openbench/runner/wandb_logger.py b/src/openbench/runner/wandb_logger.py index 0093324..5a80b22 100644 --- a/src/openbench/runner/wandb_logger.py +++ b/src/openbench/runner/wandb_logger.py @@ -19,6 +19,7 @@ BaseSampleResult, DiarizationSampleResult, GlobalResult, + SpeechGenerationSampleResult, TaskResult, TranscriptionSampleResult, ) @@ -357,3 +358,21 @@ def custom_log( ) -> dict[str, Any]: """Custom logging logic for transcription pipeline.""" return {} + + +class SpeechGenerationWandbLogger(WandbLogger[SpeechGenerationSampleResult]): + """Logger for speech-generation pipeline results. + + The base WandbLogger already serializes each sample's prediction via + `prediction.to_annotation_file`, which for GeneratedAudio copies the + WAV under the predictions artifact directory. No additional custom + logging is needed at this stage. + """ + + def custom_log( + self, + global_results: list[GlobalResult], + task_results: list[TaskResult], + sample_results: list[SpeechGenerationSampleResult], + ) -> dict[str, Any]: + return {} diff --git a/tests/cli/test_pipeline_config_overrides.py b/tests/cli/test_pipeline_config_overrides.py new file mode 100644 index 0000000..1f2e60e --- /dev/null +++ b/tests/cli/test_pipeline_config_overrides.py @@ -0,0 +1,126 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. + +"""Tests for parse_pipeline_config_overrides. + +The helper turns the raw `--pipeline-config key=value` strings collected +by typer into a dict that's later merged into a pipeline alias's default +config and passed to PipelineRegistry.create_pipeline. Values stay as +strings — Pydantic does the type coercion when the config model is +instantiated. + +These tests exercise the parser surface and verify that downstream +Pydantic coercion handles ints, floats, bools, and string values for a +representative speech-generation config. +""" + +import unittest + +import typer +from pydantic import BaseModel + +from openbench.cli.command_utils import parse_pipeline_config_overrides +from openbench.pipeline.speech_generation.common import SpeechGenerationConfig + + +def _build_config(**overrides) -> SpeechGenerationConfig: + """Build a SpeechGenerationConfig with required fields filled in.""" + base = {"cli_path": "/fake/whisperkit-cli"} + base.update(overrides) + return SpeechGenerationConfig(**base) + + +class TestParsePipelineConfigOverrides(unittest.TestCase): + """Surface-level parsing tests.""" + + def test_returns_empty_dict_when_none(self) -> None: + self.assertEqual(parse_pipeline_config_overrides(None), {}) + + def test_returns_empty_dict_when_empty_list(self) -> None: + self.assertEqual(parse_pipeline_config_overrides([]), {}) + + def test_single_string_pair(self) -> None: + self.assertEqual( + parse_pipeline_config_overrides(["speaker=serena"]), + {"speaker": "serena"}, + ) + + def test_multiple_pairs(self) -> None: + self.assertEqual( + parse_pipeline_config_overrides(["speaker=serena", "seed=42", "temperature=0.7"]), + {"speaker": "serena", "seed": "42", "temperature": "0.7"}, + ) + + def test_value_with_equals_sign_is_preserved(self) -> None: + # Splits on the first '=' only, so the value can itself contain '='. + self.assertEqual( + parse_pipeline_config_overrides(["tokenizer=org/repo=v2"]), + {"tokenizer": "org/repo=v2"}, + ) + + def test_value_can_be_empty_string(self) -> None: + self.assertEqual(parse_pipeline_config_overrides(["language="]), {"language": ""}) + + def test_key_is_stripped_of_surrounding_whitespace(self) -> None: + self.assertEqual(parse_pipeline_config_overrides([" seed =42"]), {"seed": "42"}) + + def test_missing_equals_raises_bad_parameter(self) -> None: + with self.assertRaises(typer.BadParameter) as ctx: + parse_pipeline_config_overrides(["seed42"]) + self.assertIn("Expected key=value", str(ctx.exception)) + + def test_empty_key_raises_bad_parameter(self) -> None: + with self.assertRaises(typer.BadParameter) as ctx: + parse_pipeline_config_overrides(["=42"]) + self.assertIn("Empty key", str(ctx.exception)) + + def test_later_value_overrides_earlier_value_for_same_key(self) -> None: + self.assertEqual(parse_pipeline_config_overrides(["seed=1", "seed=2"]), {"seed": "2"}) + + +class TestPydanticCoercionForOverrides(unittest.TestCase): + """Verify the parser's string output coerces correctly when Pydantic builds the config. + + These guard the `--pipeline-config` user contract advertised in the + flag's help text: ints, floats, strings, and bools all "just work". + """ + + def test_int_string_coerces_to_int(self) -> None: + overrides = parse_pipeline_config_overrides(["seed=42"]) + config = _build_config(**overrides) + self.assertEqual(config.seed, 42) + self.assertIsInstance(config.seed, int) + + def test_float_string_coerces_to_float(self) -> None: + overrides = parse_pipeline_config_overrides(["temperature=0.7"]) + config = _build_config(**overrides) + self.assertAlmostEqual(config.temperature, 0.7) + self.assertIsInstance(config.temperature, float) + + def test_string_value_stays_string(self) -> None: + overrides = parse_pipeline_config_overrides(["speaker=serena"]) + config = _build_config(**overrides) + self.assertEqual(config.speaker, "serena") + + def test_bool_string_coerces_to_bool_on_pydantic_field(self) -> None: + """SpeechGenerationConfig has no native bool field; use a minimal model.""" + + class _M(BaseModel): + flag: bool + + cases = [ + ("true", True), + ("True", True), + ("false", False), + ("False", False), + ("1", True), + ("0", False), + ] + for raw_value, expected in cases: + with self.subTest(raw_value=raw_value): + overrides = parse_pipeline_config_overrides([f"flag={raw_value}"]) + self.assertIs(_M(**overrides).flag, expected) + + +if __name__ == "__main__": + unittest.main() From e94ae97ffa7e50a85a4a857541671dea65e698eb Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 1 May 2026 10:56:24 -0300 Subject: [PATCH 06/15] Address review: drop common.py, metric takes TranscriptionPipeline Co-Authored-By: Claude Opus 4.7 (1M context) --- src/openbench/cli/command_utils.py | 8 +- .../speech_generation_wer.py | 142 +++++++++--------- .../pipeline/speech_generation/__init__.py | 9 +- .../pipeline/speech_generation/common.py | 0 tests/cli/test_pipeline_config_overrides.py | 10 +- 5 files changed, 82 insertions(+), 87 deletions(-) delete mode 100644 src/openbench/pipeline/speech_generation/common.py diff --git a/src/openbench/cli/command_utils.py b/src/openbench/cli/command_utils.py index 82a4b61..ecf1e85 100644 --- a/src/openbench/cli/command_utils.py +++ b/src/openbench/cli/command_utils.py @@ -22,10 +22,10 @@ def parse_pipeline_config_overrides(items: list[str] | None) -> dict[str, str]: Values are kept as raw strings. Pydantic performs the actual type coercion when the pipeline config is built — so e.g. `seed=10`, `temperature=0.9`, and `force_language=true` all work as expected - against a `SpeechGenerationConfig` (or any other Pydantic config), - because Pydantic's lax mode parses ints, floats, and the standard - boolean spellings ("true"/"false"/"yes"/"no"/"1"/"0", case-insensitive) - from strings. Quote arguments that contain spaces. + against the pipeline's Pydantic config, because Pydantic's lax mode + parses ints, floats, and the standard boolean spellings + ("true"/"false"/"yes"/"no"/"1"/"0", case-insensitive) from strings. + Quote arguments that contain spaces. Examples: --pipeline-config speaker=serena diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py index 4fdd7a2..e26d322 100644 --- a/src/openbench/metric/word_error_metrics/speech_generation_wer.py +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -9,24 +9,27 @@ keeps the TTS pipeline free of ASR concerns — including from its timing budget — while still letting WER score generated audio. -Transcription is performed by a caller-supplied callable -`transcribe_fn(audio_path: str) -> Transcript`. When not supplied a default -WhisperKitPro-backed implementation is built lazily (parakeet-v2 ASR), so -users who only need this metric with the project's default ASR don't have -to wire anything up. A future PR will plumb metric-level configuration -through the CLI; for now constructor injection is the supported swap -point. +Transcription is performed by a caller-supplied `TranscriptionPipeline` +(any pipeline registered with `PipelineType.TRANSCRIPTION`). When not +supplied, a default WhisperKitPro / parakeet-v2 pipeline is built lazily +on first use so users on the project's default ASR don't have to wire +anything up. A future PR will plumb metric-level configuration through +the CLI; for now constructor injection is the supported swap point. """ -import json import os from pathlib import Path -from typing import Callable import coremltools as ct +import librosa from argmaxtools.utils import get_logger -from ...engine.whisperkitpro_engine import WhisperKitPro, WhisperKitProConfig, WhisperKitProInput +from ...dataset import TranscriptionSample +from ...pipeline import Pipeline +from ...pipeline.transcription.transcription_whisperkitpro import ( + WhisperKitProTranscriptionConfig, + WhisperKitProTranscriptionPipeline, +) from ...pipeline_prediction import GeneratedAudio, Transcript from ...types import PipelineType from ..metric import MetricOptions @@ -37,65 +40,46 @@ logger = get_logger(__name__) -def _whisperkitpro_transcribe_factory( - cli_path: str | None = None, - repo_id: str = "argmaxinc/parakeetkit-pro", - model_variant: str = "nvidia_parakeet-v2_476MB", -) -> Callable[[str], Transcript]: - """Build a default `transcribe_fn` backed by the WhisperKitPro engine. +def _build_default_transcription_pipeline() -> Pipeline: + """Build the default WhisperKitPro / parakeet-v2 transcription pipeline. - The engine (and underlying model) is constructed once and reused - across every call to the returned function. + This is the project's default ASR for evaluating TTS output; pass an + explicit `transcription_pipeline=` to the metric to use a different + backend. """ - cli_path = cli_path or os.getenv("WHISPERKITPRO_CLI_PATH") + cli_path = os.getenv("WHISPERKITPRO_CLI_PATH") if not cli_path: raise RuntimeError( - "SpeechGenerationWordErrorRate default transcription requires " - "WHISPERKITPRO_CLI_PATH (or an explicit cli_path). Pass " - "transcribe_fn= to the metric to use a different backend." + "SpeechGenerationWordErrorRate's default transcription requires " + "WHISPERKITPRO_CLI_PATH. Pass transcription_pipeline= to the metric " + "to use a different ASR backend." ) - engine_config = WhisperKitProConfig( - repo_id=repo_id, - model_variant=model_variant, - word_timestamps=True, - chunking_strategy="vad", + config = WhisperKitProTranscriptionConfig( + cli_path=cli_path, + repo_id="argmaxinc/parakeetkit-pro", + model_variant="nvidia_parakeet-v2_476MB", audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, ) - engine = WhisperKitPro(cli_path=cli_path, transcription_config=engine_config) - - def transcribe(audio_path: str) -> Transcript: - engine_output = engine(WhisperKitProInput(audio_path=Path(audio_path), keep_audio=False)) - - json_path = engine_output.json_report_path - try: - if not json_path.exists(): - raise RuntimeError(f"Transcription report not found at {json_path}") - with json_path.open("r") as f: - data = json.load(f) - - words, starts, ends = [], [], [] - for seg in data.get("segments", []): - for w in seg.get("words", []): - words.append(w["word"]) - if "start" in w: - starts.append(w["start"]) - if "end" in w: - ends.append(w["end"]) - - return Transcript.from_words_info( - words=words, - start=starts if starts else None, - end=ends if ends else None, - ) - finally: - json_path.unlink(missing_ok=True) - srt_path = engine_output.srt_report_path - if srt_path is not None: - Path(srt_path).unlink(missing_ok=True) + return WhisperKitProTranscriptionPipeline(config) + - return transcribe +def _build_transcription_sample(audio_path: str) -> TranscriptionSample: + """Wrap a WAV on disk in a TranscriptionSample so a pipeline can consume it. + + The reference is a no-op `Transcript` — transcription pipelines only + read it via `parse_input` for fields like keyword boosting (which we + aren't using here), not as ground truth. + """ + waveform, sample_rate = librosa.load(audio_path, sr=None) + return TranscriptionSample( + audio_name=Path(audio_path).stem, + waveform=waveform, + sample_rate=int(sample_rate), + reference=Transcript(words=[]), + extra_info={}, + ) @MetricRegistry.register_metric(PipelineType.SPEECH_GENERATION, MetricOptions.WER) @@ -103,29 +87,37 @@ class SpeechGenerationWordErrorRate(WordErrorRate): """WER metric for speech-generation pipelines. Args: - transcribe_fn: Callable taking an audio file path and returning a - `Transcript`. If `None`, a default WhisperKitPro+parakeet-v2 - transcriber is built lazily on first use. + transcription_pipeline: Any TRANSCRIPTION-typed `Pipeline`. Its + `__call__(sample) -> PipelineOutput[Transcript]` is invoked + on the generated audio. If `None`, a default WhisperKitPro / + parakeet-v2 pipeline is built lazily on first use. **kwargs: Forwarded to `WordErrorRate.__init__` (e.g. `use_text_normalizer`, `english_spelling_mapping`). """ def __init__( self, - transcribe_fn: Callable[[str], Transcript] | None = None, + transcription_pipeline: Pipeline | None = None, **kwargs, ) -> None: super().__init__(**kwargs) - self._explicit_transcribe = transcribe_fn - self._default_transcribe: Callable[[str], Transcript] | None = None - - def _get_transcribe(self) -> Callable[[str], Transcript]: - if self._explicit_transcribe is not None: - return self._explicit_transcribe - if self._default_transcribe is None: - logger.info("Building default WhisperKitPro transcriber for speech-generation WER") - self._default_transcribe = _whisperkitpro_transcribe_factory() - return self._default_transcribe + if transcription_pipeline is not None and transcription_pipeline.pipeline_type != PipelineType.TRANSCRIPTION: + raise ValueError( + "SpeechGenerationWordErrorRate.transcription_pipeline must have " + f"pipeline_type=TRANSCRIPTION, got {transcription_pipeline.pipeline_type}" + ) + self._explicit_pipeline = transcription_pipeline + self._default_pipeline: Pipeline | None = None + + def _get_pipeline(self) -> Pipeline: + if self._explicit_pipeline is not None: + return self._explicit_pipeline + if self._default_pipeline is None: + logger.info( + "Building default WhisperKitPro / parakeet-v2 transcription pipeline for speech-generation WER" + ) + self._default_pipeline = _build_default_transcription_pipeline() + return self._default_pipeline def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, **kwargs) -> dict[str, int]: """Transcribe the generated audio, then delegate to WER. @@ -139,6 +131,8 @@ def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, f"SpeechGenerationWordErrorRate expected hypothesis of type GeneratedAudio, " f"got {type(hypothesis).__name__}" ) - hypothesis_transcript = self._get_transcribe()(hypothesis.audio_path) + sample = _build_transcription_sample(hypothesis.audio_path) + pipeline_output = self._get_pipeline()(sample) + hypothesis_transcript: Transcript = pipeline_output.prediction logger.info("TTS WER hypothesis transcript: " + hypothesis_transcript.get_transcript_string()[:120] + "...") return super().compute_components(reference, hypothesis_transcript, **kwargs) diff --git a/src/openbench/pipeline/speech_generation/__init__.py b/src/openbench/pipeline/speech_generation/__init__.py index 1a6bce7..e19fd6e 100644 --- a/src/openbench/pipeline/speech_generation/__init__.py +++ b/src/openbench/pipeline/speech_generation/__init__.py @@ -1,12 +1,13 @@ # For licensing see accompanying LICENSE.md file. # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. -from .common import SpeechGenerationConfig, SpeechGenerationOutput -from .speech_generation_wkp import WhisperKitSpeechGenerationPipeline +from .speech_generation_wkp import ( + WhisperKitSpeechGenerationConfig, + WhisperKitSpeechGenerationPipeline, +) __all__ = [ - "SpeechGenerationConfig", - "SpeechGenerationOutput", + "WhisperKitSpeechGenerationConfig", "WhisperKitSpeechGenerationPipeline", ] diff --git a/src/openbench/pipeline/speech_generation/common.py b/src/openbench/pipeline/speech_generation/common.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/cli/test_pipeline_config_overrides.py b/tests/cli/test_pipeline_config_overrides.py index 1f2e60e..7246ce3 100644 --- a/tests/cli/test_pipeline_config_overrides.py +++ b/tests/cli/test_pipeline_config_overrides.py @@ -20,14 +20,14 @@ from pydantic import BaseModel from openbench.cli.command_utils import parse_pipeline_config_overrides -from openbench.pipeline.speech_generation.common import SpeechGenerationConfig +from openbench.pipeline.speech_generation import WhisperKitSpeechGenerationConfig -def _build_config(**overrides) -> SpeechGenerationConfig: - """Build a SpeechGenerationConfig with required fields filled in.""" +def _build_config(**overrides) -> WhisperKitSpeechGenerationConfig: + """Build a WhisperKitSpeechGenerationConfig with required fields filled in.""" base = {"cli_path": "/fake/whisperkit-cli"} base.update(overrides) - return SpeechGenerationConfig(**base) + return WhisperKitSpeechGenerationConfig(**base) class TestParsePipelineConfigOverrides(unittest.TestCase): @@ -103,7 +103,7 @@ def test_string_value_stays_string(self) -> None: self.assertEqual(config.speaker, "serena") def test_bool_string_coerces_to_bool_on_pydantic_field(self) -> None: - """SpeechGenerationConfig has no native bool field; use a minimal model.""" + """WhisperKitSpeechGenerationConfig has no native bool field; use a minimal model.""" class _M(BaseModel): flag: bool From 842c5b502e168c26bfcec492f63a0c22124f7621 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 12:03:54 -0300 Subject: [PATCH 07/15] Add TTS to ArgmaxOpenSourceEngine, wire up argmax-oss speech-gen pipeline Extend the engine with a tts() method mirroring transcribe()/diarize(), add a new ArgmaxOpenSourceSpeechGenerationPipeline that uses it, and retire the old WhisperKit-based speech-gen pipeline (whisperkit-cli no longer exists since #99). Co-Authored-By: Claude Opus 4.7 (1M context) --- src/openbench/engine/__init__.py | 4 + src/openbench/engine/argmax_oss_engine.py | 42 +++- .../diarization/diarization_argmax_oss.py | 2 - .../orchestration/orchestration_argmax_oss.py | 2 - src/openbench/pipeline/pipeline_aliases.py | 17 +- .../pipeline/speech_generation/__init__.py | 10 +- .../speech_generation_argmax_oss.py | 175 +++++++++++++++ .../speech_generation_wkp.py | 199 ------------------ tests/cli/test_pipeline_config_overrides.py | 12 +- 9 files changed, 237 insertions(+), 226 deletions(-) create mode 100644 src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py delete mode 100644 src/openbench/pipeline/speech_generation/speech_generation_wkp.py diff --git a/src/openbench/engine/__init__.py b/src/openbench/engine/__init__.py index 04dfd78..677ebf6 100644 --- a/src/openbench/engine/__init__.py +++ b/src/openbench/engine/__init__.py @@ -5,6 +5,8 @@ DiarizeCliOutput, TranscriptionCliInput, TranscriptionCliOutput, + TtsCliInput, + TtsCliOutput, resolve_argmax_oss_cache_dir, ) from .deepgram_engine import DeepgramApi, DeepgramApiResponse @@ -33,6 +35,8 @@ "DiarizeCliOutput", "TranscriptionCliInput", "TranscriptionCliOutput", + "TtsCliInput", + "TtsCliOutput", "resolve_argmax_oss_cache_dir", "DeepgramApi", "DeepgramApiResponse", diff --git a/src/openbench/engine/argmax_oss_engine.py b/src/openbench/engine/argmax_oss_engine.py index 02aa9c2..e0b93a8 100644 --- a/src/openbench/engine/argmax_oss_engine.py +++ b/src/openbench/engine/argmax_oss_engine.py @@ -1,9 +1,7 @@ # For licensing see accompanying LICENSE.md file. # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. -"""Argmax SDK open-source CLI (`argmax-cli`) — clone/build, transcribe, and diarize.""" - -from __future__ import annotations +"""Argmax SDK open-source CLI (`argmax-cli`) — clone/build, transcribe, diarize, and tts.""" import os import subprocess @@ -79,8 +77,21 @@ class DiarizeCliOutput(BaseModel): rttm_path: Path = Field(..., description="Written RTTM path") +class TtsCliInput(BaseModel): + """Input for `argmax-cli tts`.""" + + text: str = Field(..., description="Text to synthesize.") + output_path: Path = Field(..., description="Destination audio file path (extension picks format).") + + +class TtsCliOutput(BaseModel): + """Output from `argmax-cli tts`.""" + + audio_path: Path = Field(..., description="Generated audio path.") + + class ArgmaxOpenSourceEngine: - """Resolve `argmax-cli`, then run `transcribe` / `diarize` subcommands.""" + """Resolve `argmax-cli`, then run `transcribe` / `diarize` / `tts` subcommands.""" def __init__(self, config: ArgmaxOpenSourceEngineConfig) -> None: self.config = config @@ -201,3 +212,26 @@ def diarize(self, input: DiarizeCliInput, diarize_args: list[str]) -> DiarizeCli input.audio_path.unlink(missing_ok=True) return DiarizeCliOutput(rttm_path=input.rttm_path) + + def tts(self, input: TtsCliInput, tts_args: list[str]) -> TtsCliOutput: + """Run `argmax-cli tts` with pre-built flag list (see TTS pipeline config).""" + input.output_path.parent.mkdir(parents=True, exist_ok=True) + cmd = [ + self.cli_path, + "tts", + "--text", + input.text, + "--output-path", + str(input.output_path), + *tts_args, + ] + logger.debug("Argmax OSS tts: %s", cmd) + try: + subprocess.run(cmd, check=True, capture_output=True, text=True) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"argmax-cli tts failed: {e.stderr}") from e + + if not input.output_path.exists(): + raise RuntimeError(f"argmax-cli tts returned 0 but no audio at {input.output_path}") + + return TtsCliOutput(audio_path=input.output_path) diff --git a/src/openbench/pipeline/diarization/diarization_argmax_oss.py b/src/openbench/pipeline/diarization/diarization_argmax_oss.py index 74773d3..338b625 100644 --- a/src/openbench/pipeline/diarization/diarization_argmax_oss.py +++ b/src/openbench/pipeline/diarization/diarization_argmax_oss.py @@ -3,8 +3,6 @@ """Diarization via Argmax SDK open-source `argmax-cli diarize`.""" -from __future__ import annotations - from pathlib import Path from typing import Callable diff --git a/src/openbench/pipeline/orchestration/orchestration_argmax_oss.py b/src/openbench/pipeline/orchestration/orchestration_argmax_oss.py index ea468fe..d7528b2 100644 --- a/src/openbench/pipeline/orchestration/orchestration_argmax_oss.py +++ b/src/openbench/pipeline/orchestration/orchestration_argmax_oss.py @@ -3,8 +3,6 @@ """Orchestration: `argmax-cli transcribe --diarization`; prediction from RTTM-like verbose log (word + speaker, no timestamps).""" -from __future__ import annotations - from pathlib import Path from typing import Callable diff --git a/src/openbench/pipeline/pipeline_aliases.py b/src/openbench/pipeline/pipeline_aliases.py index 3a98be7..4783dd8 100644 --- a/src/openbench/pipeline/pipeline_aliases.py +++ b/src/openbench/pipeline/pipeline_aliases.py @@ -28,7 +28,7 @@ ) from .pipeline_registry import PipelineRegistry from .speech_generation import ( - WhisperKitSpeechGenerationPipeline, + ArgmaxOpenSourceSpeechGenerationPipeline, ) from .streaming_transcription import ( AssemblyAIStreamingPipeline, @@ -745,23 +745,24 @@ def register_pipeline_aliases() -> None: ################# SPEECH GENERATION PIPELINES ################# PipelineRegistry.register_alias( - "whisperkit-speech-generation", - WhisperKitSpeechGenerationPipeline, + "argmax-oss-speech-generation", + ArgmaxOpenSourceSpeechGenerationPipeline, default_config={ "out_dir": "./speech_generation_results", - "cli_path": os.getenv("WHISPERKIT_CLI_PATH"), "speaker": "aiden", "language": "english", + "output_format": "wav", "seed": 10, "temperature": 0.9, "top_k": 50, "max_new_tokens": 245, }, description=( - "WhisperKit speech-generation pipeline. Synthesizes audio from text prompts via whisperkit-cli TTS. " - "WER is computed by `SpeechGenerationWordErrorRate`, which transcribes the generated audio with " - "WhisperKitPro / parakeet-v2 by default and compares against the original prompt. " - "Requires `WHISPERKIT_CLI_PATH` for TTS; the default WER metric also requires `WHISPERKITPRO_CLI_PATH`." + "Argmax SDK (open source) speech-generation pipeline. Synthesizes audio from text prompts via " + "`argmax-cli tts`. Clone/build under ARGMAX_OSS_CACHE_DIR (default ~/.cache/openbench/argmax-oss) " + "unless `cli_path` is set. WER is computed by `SpeechGenerationWordErrorRate`, which transcribes " + "the generated audio with WhisperKitPro / parakeet-v2 by default and compares against the original " + "prompt; that metric requires `WHISPERKITPRO_CLI_PATH`." ), ) diff --git a/src/openbench/pipeline/speech_generation/__init__.py b/src/openbench/pipeline/speech_generation/__init__.py index e19fd6e..4a93831 100644 --- a/src/openbench/pipeline/speech_generation/__init__.py +++ b/src/openbench/pipeline/speech_generation/__init__.py @@ -1,13 +1,13 @@ # For licensing see accompanying LICENSE.md file. # Copyright (C) 2025 Argmax, Inc. All Rights Reserved. -from .speech_generation_wkp import ( - WhisperKitSpeechGenerationConfig, - WhisperKitSpeechGenerationPipeline, +from .speech_generation_argmax_oss import ( + ArgmaxOpenSourceSpeechGenerationConfig, + ArgmaxOpenSourceSpeechGenerationPipeline, ) __all__ = [ - "WhisperKitSpeechGenerationConfig", - "WhisperKitSpeechGenerationPipeline", + "ArgmaxOpenSourceSpeechGenerationConfig", + "ArgmaxOpenSourceSpeechGenerationPipeline", ] diff --git a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py new file mode 100644 index 0000000..f8f2ade --- /dev/null +++ b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py @@ -0,0 +1,175 @@ +# For licensing see accompanying LICENSE.md file. +# Copyright (C) 2026 Argmax, Inc. All Rights Reserved. + +"""Speech-generation pipeline via Argmax SDK open-source `argmax-cli tts`.""" + +from pathlib import Path +from typing import Callable, Literal + +import librosa +from argmaxtools.utils import get_logger +from pydantic import BaseModel, Field + +from ...dataset.dataset_speech_generation import SpeechGenerationSample +from ...engine.argmax_oss_engine import ( + ArgmaxOpenSourceEngine, + ArgmaxOpenSourceEngineConfig, + TtsCliInput, + TtsCliOutput, +) +from ...pipeline_prediction import GeneratedAudio +from ..base import ( + Pipeline, + PipelineConfig, + PipelineOutput, + PipelineType, + register_pipeline, +) + + +logger = get_logger(__name__) + +TEMP_TTS_AUDIO_DIR = Path("./temp_tts_audio") + + +class ArgmaxOpenSourceSpeechGenerationConfig(PipelineConfig): + """Config for the Argmax OSS speech-generation pipeline. + + Engine fields (cache_dir / commit_hash / cli_path) mirror the + transcription / diarization argmax-oss configs. TTS-specific fields + follow `argmax-cli tts` flags. + """ + + cache_dir: str | None = Field( + default=None, + description="Cache directory for argmax-oss clone + CLI build. " + "Defaults to ARGMAX_OSS_CACHE_DIR or ~/.cache/openbench/argmax-oss.", + ) + commit_hash: str | None = Field( + default=None, + description="Optional git commit pin for the clone.", + ) + cli_path: str | None = Field( + default=None, + description="Prebuilt argmax-cli path; skips clone/build.", + ) + speaker: str = Field(default="aiden", description="--speaker.") + language: str = Field(default="english", description="--language.") + output_format: Literal["wav", "m4a"] = Field( + default="wav", + description="--output-format. WAV is preferred so the WER metric can decode without extra deps.", + ) + seed: int | None = Field(default=None, description="--seed for reproducible output.") + temperature: float = Field(default=0.9, description="--temperature.") + top_k: int = Field(default=50, description="--top-k.") + max_new_tokens: int = Field(default=245, description="--max-new-tokens (RVQ frames).") + instruction: str | None = Field( + default=None, + description="--instruction (style hint, e.g. 'Speak slowly'). 1.7B model only.", + ) + model: Literal["0.6b", "1.7b"] | None = Field( + default=None, + description="--model preset (0.6b or 1.7b). Leave None to use CLI default.", + ) + models_path: str | None = Field(default=None, description="--models-path (local model dir).") + model_repo: str | None = Field(default=None, description="--model-repo (HF repo).") + version_dir: str | None = Field(default=None, description="--version-dir (overrides --model preset).") + tokenizer: str | None = Field(default=None, description="--tokenizer (HF repo or local path).") + + def generate_tts_cli_args(self) -> list[str]: + args: list[str] = [ + "--speaker", + self.speaker, + "--language", + self.language, + "--output-format", + self.output_format, + "--temperature", + str(self.temperature), + "--top-k", + str(self.top_k), + "--max-new-tokens", + str(self.max_new_tokens), + ] + if self.seed is not None: + args.extend(["--seed", str(self.seed)]) + if self.instruction is not None: + args.extend(["--instruction", self.instruction]) + if self.model is not None: + args.extend(["--model", self.model]) + if self.models_path is not None: + args.extend(["--models-path", self.models_path]) + if self.model_repo is not None: + args.extend(["--model-repo", self.model_repo]) + if self.version_dir is not None: + args.extend(["--version-dir", self.version_dir]) + if self.tokenizer is not None: + args.extend(["--tokenizer", self.tokenizer]) + return args + + +class SpeechGenerationInput(BaseModel): + """Input for the speech-generation pipeline.""" + + text: str = Field(..., description="Text prompt to generate speech from.") + audio_name: str = Field(..., description="Unique identifier for this sample (used for temp file naming).") + + +@register_pipeline +class ArgmaxOpenSourceSpeechGenerationPipeline(Pipeline): + """Speech-generation pipeline using `argmax-cli tts` via `ArgmaxOpenSourceEngine`. + + For each sample, the pipeline: + + 1. Builds an `argmax-cli tts` flag list from the config. + 2. Synthesizes audio to a temp directory under cwd. + 3. Measures the duration via librosa. + 4. Returns a `GeneratedAudio` prediction (path + duration). + + WER scoring is performed by `SpeechGenerationWordErrorRate`, not here, + so the pipeline's reported `prediction_time` reflects TTS only. + """ + + _config_class = ArgmaxOpenSourceSpeechGenerationConfig + pipeline_type = PipelineType.SPEECH_GENERATION + + def build_pipeline(self) -> Callable[[SpeechGenerationInput], GeneratedAudio]: + engine = ArgmaxOpenSourceEngine( + ArgmaxOpenSourceEngineConfig( + cache_dir=self.config.cache_dir, + commit_hash=self.config.commit_hash, + cli_path=self.config.cli_path, + ) + ) + tts_args = self.config.generate_tts_cli_args() + suffix = f".{self.config.output_format}" + + def generate(inp: SpeechGenerationInput) -> GeneratedAudio: + TEMP_TTS_AUDIO_DIR.mkdir(parents=True, exist_ok=True) + audio_path = TEMP_TTS_AUDIO_DIR / f"{inp.audio_name}{suffix}" + try: + output: TtsCliOutput = engine.tts( + TtsCliInput(text=inp.text, output_path=audio_path), + tts_args, + ) + duration = float(librosa.get_duration(path=str(output.audio_path))) + logger.info("Generated TTS audio: %s (%.2fs)", output.audio_path, duration) + return GeneratedAudio( + audio_path=str(output.audio_path), + duration=duration, + ) + except Exception: + # Clean up partial output so the temp dir doesn't grow across retries. + audio_path.unlink(missing_ok=True) + raise + + return generate + + def parse_input(self, input_sample: SpeechGenerationSample) -> SpeechGenerationInput: + return SpeechGenerationInput( + text=input_sample.reference.get_transcript_string(), + audio_name=input_sample.audio_name, + ) + + def parse_output(self, output: GeneratedAudio) -> PipelineOutput[GeneratedAudio]: + return PipelineOutput[GeneratedAudio](prediction=output) diff --git a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py b/src/openbench/pipeline/speech_generation/speech_generation_wkp.py deleted file mode 100644 index 49d2678..0000000 --- a/src/openbench/pipeline/speech_generation/speech_generation_wkp.py +++ /dev/null @@ -1,199 +0,0 @@ -# For licensing see accompanying LICENSE.md file. -# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. - -"""Speech-generation pipeline using the WhisperKit CLI. - -The pipeline shells out to `whisperkit-cli tts` to synthesize a WAV from -a text prompt and returns a `GeneratedAudio` prediction containing the -file path and the measured duration. Transcription / WER scoring is -performed by the `SpeechGenerationWordErrorRate` metric, not here, so -the pipeline's reported `prediction_time` reflects TTS only. -""" - -import subprocess -from pathlib import Path -from typing import Callable - -import librosa -from argmaxtools.utils import get_logger -from pydantic import BaseModel, Field - -from ...dataset.dataset_speech_generation import SpeechGenerationSample -from ...pipeline_prediction import GeneratedAudio -from ..base import ( - Pipeline, - PipelineConfig, - PipelineOutput, - PipelineType, - register_pipeline, -) - - -logger = get_logger(__name__) - -TEMP_TTS_AUDIO_DIR = Path("./temp_tts_audio") - - -class WhisperKitSpeechGenerationConfig(PipelineConfig): - """Config for the WhisperKit speech-generation pipeline. - - All fields are specific to whisperkit-cli; we don't currently share - anything across TTS providers, so this config lives next to the - pipeline rather than under a common base. - """ - - cli_path: str = Field( - ..., - description="Path to the whisperkit-cli binary (used for TTS generation).", - ) - speaker: str = Field( - default="aiden", - description="Speaker voice for TTS generation.", - ) - language: str = Field( - default="english", - description="Language for TTS generation.", - ) - seed: int | None = Field( - default=None, - description="Random seed for reproducible output.", - ) - temperature: float = Field( - default=0.9, - description="Sampling temperature for TTS.", - ) - top_k: int = Field( - default=50, - description="Top-k sampling for TTS.", - ) - max_new_tokens: int = Field( - default=245, - description="Max RVQ frames to generate.", - ) - models_path: str | None = Field( - default=None, - description="Local model directory for TTS.", - ) - model_repo: str | None = Field( - default=None, - description="HF repo for TTS model download.", - ) - version_dir: str | None = Field( - default=None, - description="TTS model version directory.", - ) - tokenizer: str | None = Field( - default=None, - description="HF tokenizer repo or local path.", - ) - - -class SpeechGenerationInput(BaseModel): - """Input for the speech generation pipeline.""" - - text: str = Field( - ..., - description="Text prompt to generate speech from.", - ) - audio_name: str = Field( - ..., - description="Unique identifier for this sample (used for temp file naming).", - ) - - -@register_pipeline -class WhisperKitSpeechGenerationPipeline(Pipeline): - """Speech-generation pipeline using the WhisperKit CLI. - - For each sample, the pipeline: - - 1. Builds a `whisperkit-cli tts` invocation from the config. - 2. Synthesizes the WAV to a temp directory under cwd. - 3. Measures the duration via librosa. - 4. Returns a `GeneratedAudio` prediction (path + duration). - - The generated WAV is left in place so the WER metric can transcribe - it and the wandb logger can copy it into the predictions artifact. - On TTS failure the partial WAV (if any) is removed so the temp dir - doesn't accumulate stale files across retries. - """ - - _config_class = WhisperKitSpeechGenerationConfig - pipeline_type = PipelineType.SPEECH_GENERATION - - def build_pipeline(self) -> Callable[[SpeechGenerationInput], GeneratedAudio]: - config = self.config - - def generate(input: SpeechGenerationInput) -> GeneratedAudio: - TEMP_TTS_AUDIO_DIR.mkdir(parents=True, exist_ok=True) - audio_path = TEMP_TTS_AUDIO_DIR / f"{input.audio_name}.wav" - - tts_cmd = [ - config.cli_path, - "tts", - "--text", - input.text, - "--speaker", - config.speaker, - "--language", - config.language, - "--output-path", - str(audio_path), - "--temperature", - str(config.temperature), - "--top-k", - str(config.top_k), - "--max-new-tokens", - str(config.max_new_tokens), - ] - if config.seed is not None: - tts_cmd.extend(["--seed", str(config.seed)]) - if config.models_path is not None: - tts_cmd.extend(["--models-path", config.models_path]) - if config.model_repo is not None: - tts_cmd.extend(["--model-repo", config.model_repo]) - if config.version_dir is not None: - tts_cmd.extend(["--version-dir", config.version_dir]) - if config.tokenizer is not None: - tts_cmd.extend(["--tokenizer", config.tokenizer]) - - logger.debug(f"Running TTS: {' '.join(tts_cmd)}") - - try: - tts_result = subprocess.run(tts_cmd, capture_output=True, text=True) - if tts_result.returncode != 0: - raise RuntimeError( - "whisperkit-cli tts failed " - f"(exit {tts_result.returncode}):\n" - f" stdout: {tts_result.stdout[:500]}\n" - f" stderr: {tts_result.stderr[:500]}" - ) - if not audio_path.exists(): - raise RuntimeError(f"TTS completed but audio file not found at {audio_path}") - - duration = float(librosa.get_duration(path=str(audio_path))) - logger.info(f"Generated TTS audio: {audio_path} ({duration:.2f}s)") - - return GeneratedAudio( - audio_path=str(audio_path), - duration=duration, - ) - except Exception: - # On any failure during synthesis or duration probing, - # clean up the partial WAV so the temp dir doesn't grow. - audio_path.unlink(missing_ok=True) - raise - - return generate - - def parse_input(self, input_sample: SpeechGenerationSample) -> SpeechGenerationInput: - """Extract the text prompt and a stable identifier from the sample.""" - text = input_sample.reference.get_transcript_string() - return SpeechGenerationInput( - text=text, - audio_name=input_sample.audio_name, - ) - - def parse_output(self, output: GeneratedAudio) -> PipelineOutput[GeneratedAudio]: - """Wrap the generated audio in a `PipelineOutput`.""" - return PipelineOutput[GeneratedAudio](prediction=output) diff --git a/tests/cli/test_pipeline_config_overrides.py b/tests/cli/test_pipeline_config_overrides.py index 7246ce3..a3ce1c8 100644 --- a/tests/cli/test_pipeline_config_overrides.py +++ b/tests/cli/test_pipeline_config_overrides.py @@ -20,14 +20,14 @@ from pydantic import BaseModel from openbench.cli.command_utils import parse_pipeline_config_overrides -from openbench.pipeline.speech_generation import WhisperKitSpeechGenerationConfig +from openbench.pipeline.speech_generation import ArgmaxOpenSourceSpeechGenerationConfig -def _build_config(**overrides) -> WhisperKitSpeechGenerationConfig: - """Build a WhisperKitSpeechGenerationConfig with required fields filled in.""" - base = {"cli_path": "/fake/whisperkit-cli"} +def _build_config(**overrides) -> ArgmaxOpenSourceSpeechGenerationConfig: + """Build an ArgmaxOpenSourceSpeechGenerationConfig with sensible defaults.""" + base = {"cli_path": "/fake/argmax-cli"} base.update(overrides) - return WhisperKitSpeechGenerationConfig(**base) + return ArgmaxOpenSourceSpeechGenerationConfig(**base) class TestParsePipelineConfigOverrides(unittest.TestCase): @@ -103,7 +103,7 @@ def test_string_value_stays_string(self) -> None: self.assertEqual(config.speaker, "serena") def test_bool_string_coerces_to_bool_on_pydantic_field(self) -> None: - """WhisperKitSpeechGenerationConfig has no native bool field; use a minimal model.""" + """ArgmaxOpenSourceSpeechGenerationConfig has no native bool field; use a minimal model.""" class _M(BaseModel): flag: bool From 0d8122862c6fe10dc6bbd8fc62c9c4b891c74371 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 12:23:39 -0300 Subject: [PATCH 08/15] TTS WER metric takes TranscriptionConfig; registry builds the pipeline MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add PipelineRegistry.create_pipeline_from_config that reverse-looks up a registered Pipeline by matching _config_class, then instantiates it. Refactor SpeechGenerationWordErrorRate to accept a TranscriptionConfig instead of a fully-built Pipeline — callers no longer have to construct the transcription pipeline themselves; the metric does it lazily on first use. Default remains WhisperKitPro / parakeet-v2. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../speech_generation_wer.py | 68 +++++++++---------- src/openbench/pipeline/pipeline_registry.py | 20 +++++- 2 files changed, 50 insertions(+), 38 deletions(-) diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py index e26d322..628a8e9 100644 --- a/src/openbench/metric/word_error_metrics/speech_generation_wer.py +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -1,5 +1,5 @@ # For licensing see accompanying LICENSE.md file. -# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. +# Copyright (C) 2026 Argmax, Inc. All Rights Reserved. """Word Error Rate for speech-generation pipelines. @@ -9,12 +9,12 @@ keeps the TTS pipeline free of ASR concerns — including from its timing budget — while still letting WER score generated audio. -Transcription is performed by a caller-supplied `TranscriptionPipeline` -(any pipeline registered with `PipelineType.TRANSCRIPTION`). When not -supplied, a default WhisperKitPro / parakeet-v2 pipeline is built lazily -on first use so users on the project's default ASR don't have to wire -anything up. A future PR will plumb metric-level configuration through -the CLI; for now constructor injection is the supported swap point. +Transcription is performed via a caller-supplied `TranscriptionConfig` +(any subclass of TranscriptionConfig). The metric resolves the matching +`Pipeline` subclass through `PipelineRegistry.create_pipeline_from_config`, +so callers never have to construct the Pipeline themselves. When no +config is supplied, a default WhisperKitPro / parakeet-v2 config is used +so users on the project's default ASR don't have to wire anything up. """ import os @@ -26,10 +26,9 @@ from ...dataset import TranscriptionSample from ...pipeline import Pipeline -from ...pipeline.transcription.transcription_whisperkitpro import ( - WhisperKitProTranscriptionConfig, - WhisperKitProTranscriptionPipeline, -) +from ...pipeline.pipeline_registry import PipelineRegistry +from ...pipeline.transcription.common import TranscriptionConfig +from ...pipeline.transcription.transcription_whisperkitpro import WhisperKitProTranscriptionConfig from ...pipeline_prediction import GeneratedAudio, Transcript from ...types import PipelineType from ..metric import MetricOptions @@ -40,29 +39,27 @@ logger = get_logger(__name__) -def _build_default_transcription_pipeline() -> Pipeline: - """Build the default WhisperKitPro / parakeet-v2 transcription pipeline. +def _default_transcription_config() -> WhisperKitProTranscriptionConfig: + """Build the default WhisperKitPro / parakeet-v2 transcription config. - This is the project's default ASR for evaluating TTS output; pass an - explicit `transcription_pipeline=` to the metric to use a different + Pass an explicit `transcription_config=` to the metric to use a different backend. """ cli_path = os.getenv("WHISPERKITPRO_CLI_PATH") if not cli_path: raise RuntimeError( "SpeechGenerationWordErrorRate's default transcription requires " - "WHISPERKITPRO_CLI_PATH. Pass transcription_pipeline= to the metric " + "WHISPERKITPRO_CLI_PATH. Pass transcription_config= to the metric " "to use a different ASR backend." ) - config = WhisperKitProTranscriptionConfig( + return WhisperKitProTranscriptionConfig( cli_path=cli_path, repo_id="argmaxinc/parakeetkit-pro", model_variant="nvidia_parakeet-v2_476MB", audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, ) - return WhisperKitProTranscriptionPipeline(config) def _build_transcription_sample(audio_path: str) -> TranscriptionSample: @@ -87,37 +84,34 @@ class SpeechGenerationWordErrorRate(WordErrorRate): """WER metric for speech-generation pipelines. Args: - transcription_pipeline: Any TRANSCRIPTION-typed `Pipeline`. Its - `__call__(sample) -> PipelineOutput[Transcript]` is invoked - on the generated audio. If `None`, a default WhisperKitPro / - parakeet-v2 pipeline is built lazily on first use. + transcription_config: Any `TranscriptionConfig` subclass instance. + The matching transcription `Pipeline` is built lazily on first + use via `PipelineRegistry.create_pipeline_from_config`. If `None`, + a default WhisperKitPro / parakeet-v2 config is used. **kwargs: Forwarded to `WordErrorRate.__init__` (e.g. `use_text_normalizer`, `english_spelling_mapping`). """ def __init__( self, - transcription_pipeline: Pipeline | None = None, + transcription_config: TranscriptionConfig | None = None, **kwargs, ) -> None: super().__init__(**kwargs) - if transcription_pipeline is not None and transcription_pipeline.pipeline_type != PipelineType.TRANSCRIPTION: - raise ValueError( - "SpeechGenerationWordErrorRate.transcription_pipeline must have " - f"pipeline_type=TRANSCRIPTION, got {transcription_pipeline.pipeline_type}" + if transcription_config is not None and not isinstance(transcription_config, TranscriptionConfig): + raise TypeError( + "SpeechGenerationWordErrorRate.transcription_config must be a " + f"TranscriptionConfig subclass, got {type(transcription_config).__name__}" ) - self._explicit_pipeline = transcription_pipeline - self._default_pipeline: Pipeline | None = None + self._transcription_config = transcription_config + self._pipeline: Pipeline | None = None def _get_pipeline(self) -> Pipeline: - if self._explicit_pipeline is not None: - return self._explicit_pipeline - if self._default_pipeline is None: - logger.info( - "Building default WhisperKitPro / parakeet-v2 transcription pipeline for speech-generation WER" - ) - self._default_pipeline = _build_default_transcription_pipeline() - return self._default_pipeline + if self._pipeline is None: + config = self._transcription_config or _default_transcription_config() + logger.info("Building transcription pipeline for speech-generation WER: %s", type(config).__name__) + self._pipeline = PipelineRegistry.create_pipeline_from_config(config) + return self._pipeline def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, **kwargs) -> dict[str, int]: """Transcribe the generated audio, then delegate to WER. diff --git a/src/openbench/pipeline/pipeline_registry.py b/src/openbench/pipeline/pipeline_registry.py index ae19f5c..b3f5ada 100644 --- a/src/openbench/pipeline/pipeline_registry.py +++ b/src/openbench/pipeline/pipeline_registry.py @@ -5,7 +5,7 @@ from typing import Any, ClassVar from ..types import PipelineType -from .base import Pipeline +from .base import Pipeline, PipelineConfig @dataclass @@ -92,6 +92,24 @@ def create_pipeline(cls, name: str, config: dict[str, Any] | None = None) -> Pip # Create config instance using the pipeline's config class return pipeline_class.from_dict(final_config) + @classmethod + def create_pipeline_from_config(cls, config: PipelineConfig) -> Pipeline: + """Instantiate the pipeline whose `_config_class` matches `type(config)`. + + Useful when a caller already has a typed config in hand (e.g. a metric + that needs to spin up a transcription pipeline) and doesn't want to + construct the Pipeline subclass directly. Looks up registered pipeline + classes by `_config_class` identity. + """ + config_type = type(config) + for pipeline_class in cls._pipelines.values(): + if pipeline_class._config_class is config_type: + return pipeline_class(config) + raise ValueError( + f"No registered pipeline has _config_class == {config_type.__name__}. " + f"Registered configs: {[c._config_class.__name__ for c in cls._pipelines.values()]}" + ) + @classmethod def get_pipeline_type(cls, name: str) -> PipelineType: """Get the pipeline type for a given pipeline name.""" From c9fc5a349bbf0b9584ff8c5de4f0255d81ebe8b2 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 13:02:44 -0300 Subject: [PATCH 09/15] TTS WER metric: accept str alias or TranscriptionConfig, default to argmax-oss Switch the default ASR from WhisperKitPro / parakeet-v2 to the Argmax OSS 'whisperkit-large-v3-turbo' alias so the metric works out of the box without WHISPERKITPRO_CLI_PATH. The transcription argument now also accepts a pipeline alias string (resolved via PipelineRegistry.create_pipeline) in addition to a TranscriptionConfig, so callers can point at any registered alias without constructing a config. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../speech_generation_wer.py | 73 ++++++++----------- 1 file changed, 32 insertions(+), 41 deletions(-) diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py index 628a8e9..8a08c30 100644 --- a/src/openbench/metric/word_error_metrics/speech_generation_wer.py +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -9,18 +9,16 @@ keeps the TTS pipeline free of ASR concerns — including from its timing budget — while still letting WER score generated audio. -Transcription is performed via a caller-supplied `TranscriptionConfig` -(any subclass of TranscriptionConfig). The metric resolves the matching -`Pipeline` subclass through `PipelineRegistry.create_pipeline_from_config`, -so callers never have to construct the Pipeline themselves. When no -config is supplied, a default WhisperKitPro / parakeet-v2 config is used -so users on the project's default ASR don't have to wire anything up. +Transcription can be specified either as a `TranscriptionConfig` instance +(resolved via `PipelineRegistry.create_pipeline_from_config`) or as a +pipeline alias string (resolved via `PipelineRegistry.create_pipeline`). +When nothing is supplied, the metric falls back to the +`whisperkit-large-v3-turbo` Argmax OSS alias so users don't have to wire +anything up. """ -import os from pathlib import Path -import coremltools as ct import librosa from argmaxtools.utils import get_logger @@ -28,7 +26,6 @@ from ...pipeline import Pipeline from ...pipeline.pipeline_registry import PipelineRegistry from ...pipeline.transcription.common import TranscriptionConfig -from ...pipeline.transcription.transcription_whisperkitpro import WhisperKitProTranscriptionConfig from ...pipeline_prediction import GeneratedAudio, Transcript from ...types import PipelineType from ..metric import MetricOptions @@ -38,28 +35,7 @@ logger = get_logger(__name__) - -def _default_transcription_config() -> WhisperKitProTranscriptionConfig: - """Build the default WhisperKitPro / parakeet-v2 transcription config. - - Pass an explicit `transcription_config=` to the metric to use a different - backend. - """ - cli_path = os.getenv("WHISPERKITPRO_CLI_PATH") - if not cli_path: - raise RuntimeError( - "SpeechGenerationWordErrorRate's default transcription requires " - "WHISPERKITPRO_CLI_PATH. Pass transcription_config= to the metric " - "to use a different ASR backend." - ) - - return WhisperKitProTranscriptionConfig( - cli_path=cli_path, - repo_id="argmaxinc/parakeetkit-pro", - model_variant="nvidia_parakeet-v2_476MB", - audio_encoder_compute_units=ct.ComputeUnit.CPU_AND_NE, - text_decoder_compute_units=ct.ComputeUnit.CPU_AND_NE, - ) +DEFAULT_TRANSCRIPTION_ALIAS = "whisperkit-large-v3-turbo" def _build_transcription_sample(audio_path: str) -> TranscriptionSample: @@ -84,33 +60,48 @@ class SpeechGenerationWordErrorRate(WordErrorRate): """WER metric for speech-generation pipelines. Args: - transcription_config: Any `TranscriptionConfig` subclass instance. - The matching transcription `Pipeline` is built lazily on first - use via `PipelineRegistry.create_pipeline_from_config`. If `None`, - a default WhisperKitPro / parakeet-v2 config is used. + transcription_config: Either a `TranscriptionConfig` instance (the + matching `Pipeline` is built via + `PipelineRegistry.create_pipeline_from_config`) or a pipeline + alias string (resolved via `PipelineRegistry.create_pipeline`). + If `None`, defaults to the `whisperkit-large-v3-turbo` Argmax OSS + alias. The pipeline is built lazily on first use. **kwargs: Forwarded to `WordErrorRate.__init__` (e.g. `use_text_normalizer`, `english_spelling_mapping`). """ def __init__( self, - transcription_config: TranscriptionConfig | None = None, + transcription_config: TranscriptionConfig | str | None = None, **kwargs, ) -> None: super().__init__(**kwargs) - if transcription_config is not None and not isinstance(transcription_config, TranscriptionConfig): + if transcription_config is None: + transcription_config = DEFAULT_TRANSCRIPTION_ALIAS + if not isinstance(transcription_config, (TranscriptionConfig, str)): raise TypeError( "SpeechGenerationWordErrorRate.transcription_config must be a " - f"TranscriptionConfig subclass, got {type(transcription_config).__name__}" + "TranscriptionConfig instance or a pipeline alias string, got " + f"{type(transcription_config).__name__}" ) self._transcription_config = transcription_config self._pipeline: Pipeline | None = None def _get_pipeline(self) -> Pipeline: if self._pipeline is None: - config = self._transcription_config or _default_transcription_config() - logger.info("Building transcription pipeline for speech-generation WER: %s", type(config).__name__) - self._pipeline = PipelineRegistry.create_pipeline_from_config(config) + spec = self._transcription_config + if isinstance(spec, str): + logger.info("Building transcription pipeline for speech-generation WER: alias=%r", spec) + pipeline = PipelineRegistry.create_pipeline(spec) + else: + logger.info("Building transcription pipeline for speech-generation WER: %s", type(spec).__name__) + pipeline = PipelineRegistry.create_pipeline_from_config(spec) + if pipeline.pipeline_type != PipelineType.TRANSCRIPTION: + raise ValueError( + f"Resolved pipeline {type(pipeline).__name__} has pipeline_type " + f"{pipeline.pipeline_type}, expected TRANSCRIPTION." + ) + self._pipeline = pipeline return self._pipeline def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, **kwargs) -> dict[str, int]: From 62ad197059f556fc0b169160ee35070020e68c38 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 13:21:30 -0300 Subject: [PATCH 10/15] Demote per-sample TTS/WER logs from INFO to DEBUG The 'Generated TTS audio' line and the 'TTS WER hypothesis transcript' line both fire on every sample, which spams the run log and leaks the predicted transcript into INFO-level output. Drop them to DEBUG; the per-run summary printed by the benchmark runner is still INFO. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../metric/word_error_metrics/speech_generation_wer.py | 2 +- .../pipeline/speech_generation/speech_generation_argmax_oss.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py index 8a08c30..4fb801f 100644 --- a/src/openbench/metric/word_error_metrics/speech_generation_wer.py +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -119,5 +119,5 @@ def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, sample = _build_transcription_sample(hypothesis.audio_path) pipeline_output = self._get_pipeline()(sample) hypothesis_transcript: Transcript = pipeline_output.prediction - logger.info("TTS WER hypothesis transcript: " + hypothesis_transcript.get_transcript_string()[:120] + "...") + logger.debug("TTS WER hypothesis transcript: " + hypothesis_transcript.get_transcript_string()[:120] + "...") return super().compute_components(reference, hypothesis_transcript, **kwargs) diff --git a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py index f8f2ade..8f76590 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py @@ -153,7 +153,7 @@ def generate(inp: SpeechGenerationInput) -> GeneratedAudio: tts_args, ) duration = float(librosa.get_duration(path=str(output.audio_path))) - logger.info("Generated TTS audio: %s (%.2fs)", output.audio_path, duration) + logger.debug("Generated TTS audio: %s (%.2fs)", output.audio_path, duration) return GeneratedAudio( audio_path=str(output.audio_path), duration=duration, From 043f5d5c421f47e0ca4c7c8fc68401242bc2201f Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 13:52:25 -0300 Subject: [PATCH 11/15] Cache argmax-cli builds across engine instances in the same process MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ArgmaxOpenSourceEngine.__init__ now consults a module-level dict keyed by (resolved cache_root, commit_hash) before cloning and building. The second engine constructed in a run — e.g. the WER metric's transcription engine after the TTS engine — reuses the resolved cli_path instead of re-running `swift build`. Co-Authored-By: Claude Opus 4.7 (1M context) --- src/openbench/engine/argmax_oss_engine.py | 25 ++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/src/openbench/engine/argmax_oss_engine.py b/src/openbench/engine/argmax_oss_engine.py index e0b93a8..ca3fb1d 100644 --- a/src/openbench/engine/argmax_oss_engine.py +++ b/src/openbench/engine/argmax_oss_engine.py @@ -17,6 +17,12 @@ ARGMAX_OSS_PRODUCT = "argmax-cli" DEFAULT_CACHE_SUBDIR = Path(".cache") / "openbench" / "argmax-oss" +# Process-wide cache of resolved CLI binary paths, keyed by (cache_root, commit_hash). +# Avoids re-cloning and re-invoking `swift build` when multiple pipelines (e.g. a +# TTS pipeline + the WER metric's transcription pipeline) build their own engine +# instances in the same run. +_CLI_PATH_CACHE: dict[tuple[str, str | None], str] = {} + def resolve_argmax_oss_cache_dir(explicit: str | Path | None = None) -> Path: """Absolute cache root for WhisperKit clone + `argmax-cli` build.""" @@ -97,9 +103,19 @@ def __init__(self, config: ArgmaxOpenSourceEngineConfig) -> None: self.config = config if config.cli_path: self.cli_path = str(Path(config.cli_path).expanduser().resolve()) - logger.info(f"Using Argmax OSS CLI at {self.cli_path}") - else: - self.cli_path = self._clone_and_build_cli() + logger.info("Using Argmax OSS CLI at %s", self.cli_path) + return + + cache_root = resolve_argmax_oss_cache_dir(config.cache_dir) + cache_key = (str(cache_root), config.commit_hash) + cached_cli_path = _CLI_PATH_CACHE.get(cache_key) + if cached_cli_path is not None: + logger.info("Reusing cached Argmax OSS CLI at %s", cached_cli_path) + self.cli_path = cached_cli_path + return + + self.cli_path = self._clone_and_build_cli(cache_root) + _CLI_PATH_CACHE[cache_key] = self.cli_path def _build_cli(self, repo_dir: str) -> str: """Run release build (swift build -c release, not debug) and return the dir containing the binary.""" @@ -130,8 +146,7 @@ def _build_cli(self, repo_dir: str) -> str: logger.info("Built Argmax OSS CLI at %s", cli) return bin_dir - def _clone_and_build_cli(self) -> str: - cache_root = resolve_argmax_oss_cache_dir(self.config.cache_dir) + def _clone_and_build_cli(self, cache_root: Path) -> str: cache_root.mkdir(parents=True, exist_ok=True) repo_url_parts = ARGMAX_OSS_REPO_URL.rstrip("/").split("/") repo_name = repo_url_parts[-1] From ede621aabaef9d9270a5a11cccdfbfac944ed30e Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 14:40:10 -0300 Subject: [PATCH 12/15] Use Literal for speaker/language in TTS config Mirror the argmax-cli tts --help allowed values: invalid speakers / languages now fail at config construction with a clear Pydantic error instead of being passed to the CLI and failing there. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../speech_generation_argmax_oss.py | 25 +++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py index 8f76590..951f5cf 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py @@ -53,8 +53,29 @@ class ArgmaxOpenSourceSpeechGenerationConfig(PipelineConfig): default=None, description="Prebuilt argmax-cli path; skips clone/build.", ) - speaker: str = Field(default="aiden", description="--speaker.") - language: str = Field(default="english", description="--language.") + speaker: Literal[ + "ryan", + "aiden", + "ono-anna", + "sohee", + "eric", + "dylan", + "serena", + "vivian", + "uncle-fu", + ] = Field(default="aiden", description="--speaker.") + language: Literal[ + "english", + "chinese", + "japanese", + "korean", + "german", + "french", + "russian", + "portuguese", + "spanish", + "italian", + ] = Field(default="english", description="--language.") output_format: Literal["wav", "m4a"] = Field( default="wav", description="--output-format. WAV is preferred so the WER metric can decode without extra deps.", From 16b7430302408b126aa63f781db91160d1779228 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Fri, 22 May 2026 14:58:25 -0300 Subject: [PATCH 13/15] Use StrEnum for TTS speaker/language; bump min Python to 3.11 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit StrEnum (PEP 663, added in 3.11) gives us typed CLI values without the verbosity of a long Literal[...] and without an explicit .value when the string flows through to subprocess.run — StrEnum members are str subclasses. Drop 3.10 from requires-python and .python-version so we can rely on StrEnum and any other 3.11-only stdlib bits going forward. Co-Authored-By: Claude Opus 4.7 (1M context) --- .python-version | 2 +- pyproject.toml | 2 +- .../speech_generation_argmax_oss.py | 55 +- uv.lock | 924 +----------------- 4 files changed, 73 insertions(+), 910 deletions(-) diff --git a/.python-version b/.python-version index c8cfe39..2c07333 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.10 +3.11 diff --git a/pyproject.toml b/pyproject.toml index c8c1120..12931af 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "openbench" version = "0.1.0" description = "Benchmark suite for speaker diarization" authors = [{ name = "Argmax Inc.", email = "info@takeargmax.com" }] -requires-python = ">=3.10,<3.13" +requires-python = ">=3.11,<3.13" readme = "README.md" dependencies = [ "black>=24.10.0,<25", diff --git a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py index 951f5cf..0c8bda7 100644 --- a/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py +++ b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py @@ -3,6 +3,7 @@ """Speech-generation pipeline via Argmax SDK open-source `argmax-cli tts`.""" +from enum import StrEnum from pathlib import Path from typing import Callable, Literal @@ -32,6 +33,35 @@ TEMP_TTS_AUDIO_DIR = Path("./temp_tts_audio") +class TtsSpeaker(StrEnum): + """`argmax-cli tts --speaker` allowed values.""" + + RYAN = "ryan" + AIDEN = "aiden" + ONO_ANNA = "ono-anna" + SOHEE = "sohee" + ERIC = "eric" + DYLAN = "dylan" + SERENA = "serena" + VIVIAN = "vivian" + UNCLE_FU = "uncle-fu" + + +class TtsLanguage(StrEnum): + """`argmax-cli tts --language` allowed values.""" + + ENGLISH = "english" + CHINESE = "chinese" + JAPANESE = "japanese" + KOREAN = "korean" + GERMAN = "german" + FRENCH = "french" + RUSSIAN = "russian" + PORTUGUESE = "portuguese" + SPANISH = "spanish" + ITALIAN = "italian" + + class ArgmaxOpenSourceSpeechGenerationConfig(PipelineConfig): """Config for the Argmax OSS speech-generation pipeline. @@ -53,29 +83,8 @@ class ArgmaxOpenSourceSpeechGenerationConfig(PipelineConfig): default=None, description="Prebuilt argmax-cli path; skips clone/build.", ) - speaker: Literal[ - "ryan", - "aiden", - "ono-anna", - "sohee", - "eric", - "dylan", - "serena", - "vivian", - "uncle-fu", - ] = Field(default="aiden", description="--speaker.") - language: Literal[ - "english", - "chinese", - "japanese", - "korean", - "german", - "french", - "russian", - "portuguese", - "spanish", - "italian", - ] = Field(default="english", description="--language.") + speaker: TtsSpeaker = Field(default=TtsSpeaker.AIDEN, description="--speaker.") + language: TtsLanguage = Field(default=TtsLanguage.ENGLISH, description="--language.") output_format: Literal["wav", "m4a"] = Field( default="wav", description="--output-format. WAV is preferred so the WER metric can decode without extra deps.", diff --git a/uv.lock b/uv.lock index ba02fae..049d339 100644 --- a/uv.lock +++ b/uv.lock @@ -1,13 +1,11 @@ version = 1 revision = 3 -requires-python = ">=3.10, <3.13" +requires-python = ">=3.11, <3.13" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'linux'", "python_full_version >= '3.12' and sys_platform != 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and sys_platform != 'linux'", - "python_full_version < '3.11' and sys_platform != 'linux'", + "python_full_version < '3.12' and sys_platform == 'linux'", + "python_full_version < '3.12' and sys_platform != 'linux'", ] [[package]] @@ -71,7 +69,6 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", marker = "python_full_version < '3.11'" }, { name = "attrs" }, { name = "frozenlist" }, { name = "multidict" }, @@ -80,23 +77,6 @@ dependencies = [ ] sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716, upload-time = "2025-07-29T05:52:32.215Z" } wheels = [ - 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All Rights Reserved. +# Copyright (C) 2026 Argmax, Inc. All Rights Reserved. import numpy as np from typing_extensions import TypedDict diff --git a/src/openbench/engine/argmax_oss_engine.py b/src/openbench/engine/argmax_oss_engine.py index ca3fb1d..7753877 100644 --- a/src/openbench/engine/argmax_oss_engine.py +++ b/src/openbench/engine/argmax_oss_engine.py @@ -1,5 +1,5 @@ # For licensing see accompanying LICENSE.md file. -# Copyright (C) 2025 Argmax, Inc. All Rights Reserved. +# Copyright (C) 2026 Argmax, Inc. All Rights Reserved. """Argmax SDK open-source CLI (`argmax-cli`) — clone/build, transcribe, diarize, and tts.""" From 374acc23a48fd54b91a91ca37509d7d31c13ad48 Mon Sep 17 00:00:00 2001 From: Eduardo Pacheco Date: Sun, 24 May 2026 13:01:35 -0300 Subject: [PATCH 15/15] Validate ASR pipeline_type before building; drop defensive checks In SpeechGenerationWordErrorRate._get_pipeline, resolve the pipeline class via PipelineRegistry and validate pipeline_type == TRANSCRIPTION before instantiating. For argmax-oss aliases this avoids paying a full clone + swift build only to reject the pipeline afterwards. Extract get_pipeline_class_for_config out of create_pipeline_from_config so the metric can do the type check without instantiating. Also drop two paranoid checks: the isinstance(hypothesis, GeneratedAudio) guard in compute_components (type is guaranteed by MetricRegistry dispatch) and the post-subprocess output_path.exists() check in engine.tts() (subprocess.run(check=True) already raises on failure). Co-Authored-By: Claude Opus 4.7 (1M context) --- src/openbench/engine/argmax_oss_engine.py | 3 -- .../speech_generation_wer.py | 42 ++++++++++--------- src/openbench/pipeline/pipeline_registry.py | 23 ++++++---- 3 files changed, 38 insertions(+), 30 deletions(-) diff --git a/src/openbench/engine/argmax_oss_engine.py b/src/openbench/engine/argmax_oss_engine.py index 7753877..8f5ef47 100644 --- a/src/openbench/engine/argmax_oss_engine.py +++ b/src/openbench/engine/argmax_oss_engine.py @@ -246,7 +246,4 @@ def tts(self, input: TtsCliInput, tts_args: list[str]) -> TtsCliOutput: except subprocess.CalledProcessError as e: raise RuntimeError(f"argmax-cli tts failed: {e.stderr}") from e - if not input.output_path.exists(): - raise RuntimeError(f"argmax-cli tts returned 0 but no audio at {input.output_path}") - return TtsCliOutput(audio_path=input.output_path) diff --git a/src/openbench/metric/word_error_metrics/speech_generation_wer.py b/src/openbench/metric/word_error_metrics/speech_generation_wer.py index 4fb801f..b7fd86b 100644 --- a/src/openbench/metric/word_error_metrics/speech_generation_wer.py +++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py @@ -88,20 +88,29 @@ def __init__( self._pipeline: Pipeline | None = None def _get_pipeline(self) -> Pipeline: - if self._pipeline is None: - spec = self._transcription_config - if isinstance(spec, str): - logger.info("Building transcription pipeline for speech-generation WER: alias=%r", spec) - pipeline = PipelineRegistry.create_pipeline(spec) - else: - logger.info("Building transcription pipeline for speech-generation WER: %s", type(spec).__name__) - pipeline = PipelineRegistry.create_pipeline_from_config(spec) - if pipeline.pipeline_type != PipelineType.TRANSCRIPTION: - raise ValueError( - f"Resolved pipeline {type(pipeline).__name__} has pipeline_type " - f"{pipeline.pipeline_type}, expected TRANSCRIPTION." - ) - self._pipeline = pipeline + if self._pipeline is not None: + return self._pipeline + + spec = self._transcription_config + # Resolve pipeline class first so we can validate pipeline_type before + # paying the build cost (which for argmax-oss aliases means clone + swift build). + if isinstance(spec, str): + pipeline_class = PipelineRegistry.get_pipeline_class(spec) + label = f"alias={spec!r}" + else: + pipeline_class = PipelineRegistry.get_pipeline_class_for_config(type(spec)) + label = type(spec).__name__ + + if pipeline_class.pipeline_type != PipelineType.TRANSCRIPTION: + raise ValueError( + f"{pipeline_class.__name__} has pipeline_type {pipeline_class.pipeline_type}, expected TRANSCRIPTION." + ) + + logger.info("Building transcription pipeline for speech-generation WER: %s", label) + if isinstance(spec, str): + self._pipeline = PipelineRegistry.create_pipeline(spec) + else: + self._pipeline = pipeline_class(spec) return self._pipeline def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, **kwargs) -> dict[str, int]: @@ -111,11 +120,6 @@ def compute_components(self, reference: Transcript, hypothesis: GeneratedAudio, reference: Reference transcript built from the original prompt. hypothesis: Generated audio (path + duration) produced by a TTS pipeline. """ - if not isinstance(hypothesis, GeneratedAudio): - raise TypeError( - f"SpeechGenerationWordErrorRate expected hypothesis of type GeneratedAudio, " - f"got {type(hypothesis).__name__}" - ) sample = _build_transcription_sample(hypothesis.audio_path) pipeline_output = self._get_pipeline()(sample) hypothesis_transcript: Transcript = pipeline_output.prediction diff --git a/src/openbench/pipeline/pipeline_registry.py b/src/openbench/pipeline/pipeline_registry.py index b3f5ada..25ee256 100644 --- a/src/openbench/pipeline/pipeline_registry.py +++ b/src/openbench/pipeline/pipeline_registry.py @@ -93,23 +93,30 @@ def create_pipeline(cls, name: str, config: dict[str, Any] | None = None) -> Pip return pipeline_class.from_dict(final_config) @classmethod - def create_pipeline_from_config(cls, config: PipelineConfig) -> Pipeline: - """Instantiate the pipeline whose `_config_class` matches `type(config)`. + def get_pipeline_class_for_config(cls, config_type: type[PipelineConfig]) -> type[Pipeline]: + """Return the registered Pipeline subclass whose `_config_class` is `config_type`. - Useful when a caller already has a typed config in hand (e.g. a metric - that needs to spin up a transcription pipeline) and doesn't want to - construct the Pipeline subclass directly. Looks up registered pipeline - classes by `_config_class` identity. + Lets a caller inspect a pipeline's type (e.g. `pipeline_type`) before + paying the cost of instantiation. """ - config_type = type(config) for pipeline_class in cls._pipelines.values(): if pipeline_class._config_class is config_type: - return pipeline_class(config) + return pipeline_class raise ValueError( f"No registered pipeline has _config_class == {config_type.__name__}. " f"Registered configs: {[c._config_class.__name__ for c in cls._pipelines.values()]}" ) + @classmethod + def create_pipeline_from_config(cls, config: PipelineConfig) -> Pipeline: + """Instantiate the pipeline whose `_config_class` matches `type(config)`. + + Useful when a caller already has a typed config in hand (e.g. a metric + that needs to spin up a transcription pipeline) and doesn't want to + construct the Pipeline subclass directly. + """ + return cls.get_pipeline_class_for_config(type(config))(config) + @classmethod def get_pipeline_type(cls, name: str) -> PipelineType: """Get the pipeline type for a given pipeline name."""