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/cli/command_utils.py b/src/openbench/cli/command_utils.py
index 564479a..ecf1e85 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 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
+        --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 1b4f5f7..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,
@@ -176,6 +177,7 @@ 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 +210,14 @@ def run_alias_mode(
             if verbose:
                 typer.echo("✅ Force language: enabled")
 
+        # 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)
 
         ######### Build Benchmark Config #########
@@ -345,6 +355,19 @@ def evaluate(
         "--force-language",
         help="Force language hinting for compatible pipelines",
     ),
+    pipeline_config: list[str] | None = typer.Option(
+        None,
+        "--pipeline-config",
+        "-pc",
+        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:
     """Run evaluation benchmarks.
@@ -406,6 +429,7 @@ 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..d6bab78
--- /dev/null
+++ b/src/openbench/dataset/dataset_speech_generation.py
@@ -0,0 +1,76 @@
+# For licensing see accompanying LICENSE.md file.
+# Copyright (C) 2026 Argmax, Inc. All Rights Reserved.
+
+import numpy as np
+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 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.
+    """
+
+    @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 — 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]:
+        """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"]:
+            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]:
+        """Build the reference transcript from the prompt text."""
+        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/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..8f5ef47 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.
+# Copyright (C) 2026 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
@@ -19,6 +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."""
@@ -79,16 +83,39 @@ 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
         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."""
@@ -119,8 +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]
@@ -201,3 +227,23 @@ 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
+
+        return TtsCliOutput(audio_path=input.output_path)
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..b7fd86b
--- /dev/null
+++ b/src/openbench/metric/word_error_metrics/speech_generation_wer.py
@@ -0,0 +1,127 @@
+# For licensing see accompanying LICENSE.md file.
+# Copyright (C) 2026 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 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.
+"""
+
+from pathlib import Path
+
+import librosa
+from argmaxtools.utils import get_logger
+
+from ...dataset import TranscriptionSample
+from ...pipeline import Pipeline
+from ...pipeline.pipeline_registry import PipelineRegistry
+from ...pipeline.transcription.common import TranscriptionConfig
+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__)
+
+DEFAULT_TRANSCRIPTION_ALIAS = "whisperkit-large-v3-turbo"
+
+
+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)
+class SpeechGenerationWordErrorRate(WordErrorRate):
+    """WER metric for speech-generation pipelines.
+
+    Args:
+        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 | str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        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 "
+                "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 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]:
+        """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.
+        """
+        sample = _build_transcription_sample(hypothesis.audio_path)
+        pipeline_output = self._get_pipeline()(sample)
+        hypothesis_transcript: Transcript = pipeline_output.prediction
+        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/__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/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 65e6d2e..4783dd8 100644
--- a/src/openbench/pipeline/pipeline_aliases.py
+++ b/src/openbench/pipeline/pipeline_aliases.py
@@ -27,6 +27,9 @@
     WhisperXPipeline,
 )
 from .pipeline_registry import PipelineRegistry
+from .speech_generation import (
+    ArgmaxOpenSourceSpeechGenerationPipeline,
+)
 from .streaming_transcription import (
     AssemblyAIStreamingPipeline,
     DeepgramStreamingPipeline,
@@ -739,6 +742,30 @@ 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(
+        "argmax-oss-speech-generation",
+        ArgmaxOpenSourceSpeechGenerationPipeline,
+        default_config={
+            "out_dir": "./speech_generation_results",
+            "speaker": "aiden",
+            "language": "english",
+            "output_format": "wav",
+            "seed": 10,
+            "temperature": 0.9,
+            "top_k": 50,
+            "max_new_tokens": 245,
+        },
+        description=(
+            "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`."
+        ),
+    )
+
     ################# STREAMING TRANSCRIPTION PIPELINES #################
 
     PipelineRegistry.register_alias(
diff --git a/src/openbench/pipeline/pipeline_registry.py b/src/openbench/pipeline/pipeline_registry.py
index ae19f5c..25ee256 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 +92,31 @@ 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 get_pipeline_class_for_config(cls, config_type: type[PipelineConfig]) -> type[Pipeline]:
+        """Return the registered Pipeline subclass whose `_config_class` is `config_type`.
+
+        Lets a caller inspect a pipeline's type (e.g. `pipeline_type`) before
+        paying the cost of instantiation.
+        """
+        for pipeline_class in cls._pipelines.values():
+            if pipeline_class._config_class is config_type:
+                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."""
diff --git a/src/openbench/pipeline/speech_generation/__init__.py b/src/openbench/pipeline/speech_generation/__init__.py
new file mode 100644
index 0000000..4a93831
--- /dev/null
+++ b/src/openbench/pipeline/speech_generation/__init__.py
@@ -0,0 +1,13 @@
+# For licensing see accompanying LICENSE.md file.
+# Copyright (C) 2025 Argmax, Inc. All Rights Reserved.
+
+from .speech_generation_argmax_oss import (
+    ArgmaxOpenSourceSpeechGenerationConfig,
+    ArgmaxOpenSourceSpeechGenerationPipeline,
+)
+
+
+__all__ = [
+    "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..0c8bda7
--- /dev/null
+++ b/src/openbench/pipeline/speech_generation/speech_generation_argmax_oss.py
@@ -0,0 +1,205 @@
+# 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 enum import StrEnum
+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 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.
+
+    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: 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.",
+    )
+    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.debug("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_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 c05050d..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__)
@@ -33,11 +34,12 @@
     PipelineType.TRANSCRIPTION: TranscriptionSampleResult,
     PipelineType.ORCHESTRATION: TranscriptionSampleResult,
     PipelineType.STREAMING_TRANSCRIPTION: 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
@@ -64,6 +66,7 @@ def __init__(self, config: BenchmarkConfig, pipelines: list[Pipeline]):
             PipelineType.TRANSCRIPTION: TranscriptionWandbLogger,
             PipelineType.ORCHESTRATION: TranscriptionWandbLogger,
             PipelineType.STREAMING_TRANSCRIPTION: TranscriptionWandbLogger,
+            PipelineType.SPEECH_GENERATION: SpeechGenerationWandbLogger,
         }
 
     def _get_metrics(self, pipeline: Pipeline) -> dict[str, BaseMetric]:
@@ -72,7 +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):
@@ -99,7 +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/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
diff --git a/tests/cli/test_pipeline_config_overrides.py b/tests/cli/test_pipeline_config_overrides.py
new file mode 100644
index 0000000..a3ce1c8
--- /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 import ArgmaxOpenSourceSpeechGenerationConfig
+
+
+def _build_config(**overrides) -> ArgmaxOpenSourceSpeechGenerationConfig:
+    """Build an ArgmaxOpenSourceSpeechGenerationConfig with sensible defaults."""
+    base = {"cli_path": "/fake/argmax-cli"}
+    base.update(overrides)
+    return ArgmaxOpenSourceSpeechGenerationConfig(**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:
+        """ArgmaxOpenSourceSpeechGenerationConfig 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()
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" },
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@@ -153,7 +133,6 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
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