Add faster-whisper (ctranslate2) as option for Whisper annotation workflow

lhotse/bin/modes/workflows.py

@@ -1,3 +1,4 @@
+from functools import partial
 from typing import List, Optional, Union
 
 import click
@@ -55,6 +56,36 @@ def workflows():
 @click.option(
     "-d", "--device", default="cpu", help="Device on which to run the inference."
 )
+@click.option(
+    "--faster-whisper",
+    is_flag=True,
+    default=False,
+    help="If True, use faster-whisper's implementation based on CTranslate2.",
+)
+@click.option(
+    "--faster-whisper-use-vad",
+    is_flag=True,
+    default=False,
+    help="If True, use faster-whisper's built-in voice activity detection (SileroVAD)."
+    "Note: This requires onnxruntime to be installed.",
+)
+@click.option(
+    "--faster-whisper-add-alignments",
+    is_flag=True,
+    default=False,
+    help="If True, add word alignments using timestamps obtained using the cross-attention"
+    "pattern and dynamic time warping (Note: Less accurate than forced alignment).",
+)
+@click.option(
+    "--faster-whisper-compute-type",
+    default="float16",
+    help="Type to use for computation. See https://opennmt.net/CTranslate2/quantization.html.",
+)
+@click.option(
+    "--faster-whisper-num-workers",
+    default=1,
+    help="Number of workers for parallelization across multiple GPUs.",
+)
 @click.option("-j", "--jobs", default=1, help="Number of jobs for audio scanning.")
 @click.option(
     "--force-nonoverlapping/--keep-overlapping",
@@ -72,6 +103,11 @@ def annotate_with_whisper(
     device: str,
     jobs: int,
     force_nonoverlapping: bool,
+    faster_whisper: bool,
+    faster_whisper_use_vad: bool,
+    faster_whisper_compute_type: str,
+    faster_whisper_add_alignments: bool,
+    faster_whisper_num_workers: int,
 ):
     """
     Use OpenAI Whisper model to annotate either RECORDINGS_MANIFEST, RECORDINGS_DIR, or CUTS_MANIFEST.
@@ -83,7 +119,18 @@ def annotate_with_whisper(
     Note: this is an experimental feature of Lhotse, and is not guaranteed to yield
     high quality of data.
     """
-    from lhotse import annotate_with_whisper as annotate_with_whisper_
+    if faster_whisper:
+        from lhotse import annotate_with_faster_whisper
+
+        annotate_with_whisper_ = partial(
+            annotate_with_faster_whisper,
+            compute_type=faster_whisper_compute_type,
+            num_workers=faster_whisper_num_workers,
+            vad_filter=faster_whisper_use_vad,
+            add_alignments=faster_whisper_add_alignments,
+        )
+    else:
+        from lhotse import annotate_with_whisper as annotate_with_whisper_
 
     assert exactly_one_not_null(recordings_manifest, recordings_dir, cuts_manifest), (
         "Options RECORDINGS_MANIFEST, RECORDINGS_DIR, and CUTS_MANIFEST are mutually exclusive "

lhotse/workflows/__init__.py

@@ -1,3 +1,4 @@
+from .faster_whisper import annotate_with_faster_whisper
 from .forced_alignment import align_with_torchaudio
 from .meeting_simulation import *
 from .whisper import annotate_with_whisper

lhotse/workflows/faster_whisper.py

@@ -0,0 +1,232 @@
+import logging
+from concurrent.futures import as_completed
+from concurrent.futures.thread import ThreadPoolExecutor
+from typing import Any, Generator, List, Optional, Union
+
+import numpy as np
+
+from lhotse import (
+    CutSet,
+    MonoCut,
+    Recording,
+    RecordingSet,
+    SupervisionSegment,
+    add_durations,
+)
+from lhotse.qa import trim_supervisions_to_recordings
+from lhotse.supervision import AlignmentItem
+from lhotse.utils import fastcopy, is_module_available
+
+
+def annotate_with_faster_whisper(
+    manifest: Union[RecordingSet, CutSet],
+    model_name: str = "base",
+    device: str = "cpu",
+    force_nonoverlapping: bool = False,
+    download_root: Optional[str] = None,
+    compute_type: str = "default",
+    num_workers: int = 1,
+    vad_filter: bool = False,
+    add_alignments: bool = False,
+    **decode_options,
+) -> Generator[MonoCut, None, None]:
+    """
+    Use OpenAI Whisper model via faster-whisper and CTranslate2 to annotate either
+    RECORDINGS_MANIFEST, RECORDINGS_DIR, or CUTS_MANIFEST. It will perform automatic segmentation,
+    transcription, and language identification. If the first argument is a CutSet, it will
+    overwrite the supervisions with the results of the inference.
+
+    Note: this is an experimental feature of Lhotse, and is not guaranteed to yield
+    high quality of data.
+
+    See the original repo for more details: https://github.com/guillaumekln/faster-whisper
+
+    :param manifest: a ``RecordingSet`` or ``CutSet`` object.
+    :param language: specify the language if known upfront, otherwise it will be auto-detected.
+    :param model_name: one of available Whisper variants (base, medium, large, etc.).
+    :param device: Where to run the inference (cpu, cuda, etc.).
+    :param force_nonoverlapping: if True, the Whisper segment time-stamps will be processed to make
+        sure they are non-overlapping.
+    :param download_root: Not supported by faster-whisper. Argument kept to maintain compatibility
+        with annotate_with_whisper. Faster-whisper uses
+    :param compute_type: Type to use for computation.
+        See https://opennmt.net/CTranslate2/quantization.html.
+    :param num_workers: Increasing the number of workers can improve the global throughput at the
+        cost of increased memory usage.
+    :param vad_filter: If True, use faster-whisper's built-in voice activity detection (SileroVAD).
+    :param add_alignments: if True, add word alignments using timestamps obtained using the cross-
+        attention pattern and dynamic time warping (Note: Less accurate than forced alignment).
+    :param decode_options: additional options to pass to the ``whisper.transcribe`` function.
+    :return: a generator of cuts (use ``CutSet.open_writer()`` to write them).
+    """
+    assert is_module_available("faster_whisper"), (
+        "This function expects faster-whisper to be installed. "
+        "You can install it via 'pip install faster-whisper' "
+        "(see https://github.com/guillaumekln/faster-whisper/ for details)."
+    )
+    if not isinstance(manifest, RecordingSet) and not isinstance(manifest, CutSet):
+        raise ValueError("The ``manifest`` must be either a RecordingSet or a CutSet.")
+    assert not vad_filter or is_module_available("onnxruntime"), (
+        "Use of VAD requires onnxruntime to be installed. "
+        "You can install it via 'pip install onnxruntime' "
+        "(see https://github.com/guillaumekln/faster-whisper/ for details)."
+    )
+
+    model = _initialize_model(
+        model_name, device, compute_type, num_workers, download_root
+    )
+    with ThreadPoolExecutor(num_workers) as ex:
+        futures = []
+        for item in manifest:
+            futures.append(
+                ex.submit(
+                    _process_single_manifest,
+                    item,
+                    model,
+                    force_nonoverlapping,
+                    vad_filter,
+                    add_alignments,
+                    **decode_options,
+                )
+            )
+        for item in as_completed(futures):
+            yield item.result()
+
+
+def _initialize_model(
+    model_name: str,
+    device: str,
+    compute_type: str = "default",
+    num_workers: int = 1,
+    download_root: Optional[str] = None,
+):
+    import torch
+    from faster_whisper import WhisperModel
+
+    if num_workers > 1:
+        # Limit num_workers to available GPUs
+        num_workers = min(num_workers, torch.cuda.device_count())
+    device_index = list(range(num_workers))
+    model = WhisperModel(
+        model_name,
+        device=device,
+        device_index=device_index,
+        compute_type=compute_type,
+        num_workers=num_workers,
+        download_root=download_root,
+    )
+    model.logger.setLevel(logging.WARNING)
+    return model
+
+
+def _process_single_manifest(
+    manifest: Union[Recording, MonoCut],
+    model,
+    force_nonoverlapping: bool,
+    vad_filter: bool,
+    add_alignments: bool = False,
+    **decode_options,
+) -> MonoCut:
+    if isinstance(manifest, Recording):
+        if manifest.num_channels > 1:
+            logging.warning(
+                f"Skipping recording '{manifest.id}'. It has {manifest.num_channels} channels, "
+                f"but we currently only support mono input."
+            )
+            return []
+        recording_id = manifest.id
+    else:
+        recording_id = manifest.recording_id
+    audio = np.squeeze(manifest.resample(16000).load_audio())
+    segments, info = model.transcribe(
+        audio=audio,
+        word_timestamps=add_alignments,
+        vad_filter=vad_filter,
+        **decode_options,
+    )
+    # Create supervisions from segments while filtering out those with negative duration.
+    if add_alignments:
+        supervisions = [
+            SupervisionSegment(
+                id=f"{manifest.id}-{segment_id:06d}",
+                recording_id=recording_id,
+                start=round(segment.start, ndigits=8),
+                duration=add_durations(
+                    segment.end, -segment.start, sampling_rate=16000
+                ),
+                text=segment.text.strip(),
+                language=info.language,
+            ).with_alignment(
+                "word",
+                [
+                    AlignmentItem(
+                        symbol=ws.word.strip(),
+                        start=round(ws.start, ndigits=8),
+                        duration=round(ws.end - ws.start, ndigits=8),
+                        score=round(ws.probability, ndigits=3),
+                    )
+                    for ws in segment.words
+                ],
+            )
+            for segment_id, segment in enumerate(segments)
+            if segment.end - segment.start > 0
+        ]
+    else:
+        supervisions = [
+            SupervisionSegment(
+                id=f"{manifest.id}-{segment_id:06d}",
+                recording_id=recording_id,
+                start=round(segment.start, ndigits=8),
+                duration=add_durations(
+                    segment.end, -segment.start, sampling_rate=16000
+                ),
+                text=segment.text.strip(),
+                language=info.language,
+            )
+            for segment_id, segment in enumerate(segments)
+            if segment.end - segment.start > 0
+        ]
+
+    if isinstance(manifest, Recording):
+        cut = manifest.to_cut()
+        if supervisions:
+            supervisions = (
+                _postprocess_timestamps(supervisions)
+                if force_nonoverlapping
+                else supervisions
+            )
+            cut.supervisions = list(
+                trim_supervisions_to_recordings(
+                    recordings=manifest, supervisions=supervisions, verbose=False
+                )
+            )
+    else:
+        cut = fastcopy(
+            manifest,
+            supervisions=_postprocess_timestamps(supervisions)
+            if force_nonoverlapping
+            else supervisions,
+        )
+
+    return cut
+
+
+def _postprocess_timestamps(supervisions: List[SupervisionSegment]):
+    """
+    Whisper tends to have a lot of overlapping segments due to inaccurate end timestamps.
+    Under a strong assumption that the input speech is non-overlapping, we can fix that
+    by always truncating to the start timestamp of the next segment.
+    """
+    from cytoolz import sliding_window
+
+    supervisions = sorted(supervisions, key=lambda s: s.start)
+
+    if len(supervisions) < 2:
+        return supervisions
+    out = []
+    for cur, nxt in sliding_window(2, supervisions):
+        if cur.end > nxt.start:
+            cur = cur.trim(end=nxt.start)
+        out.append(cur)
+    out.append(nxt)
+    return out