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Implement VadOptions (#198)

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* Implement VadOptions

* Fix line too long

./faster_whisper/transcribe.py:226:101: E501 line too long (111 > 100 characters)

* Reformatted files with black

* black .\faster_whisper\vad.py    
* black .\faster_whisper\transcribe.py

* Fix import order with isort

* isort .\faster_whisper\vad.py
* isort .\faster_whisper\transcribe.py

* Made recommended changes

Recommended in https://github.com/guillaumekln/faster-whisper/pull/198

* Fix typing of vad_options argument

---------

Co-authored-by: Guillaume Klein <guillaumekln@users.noreply.github.com>
This commit is contained in:
FlippFuzz
2023-05-09 18:47:02 +08:00
committed by GitHub
parent d889345e07
commit 5d8f3e2d90
3 changed files with 50 additions and 24 deletions
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16
faster_whisper/transcribe.py
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@@ -15,6 +15,7 @@ from faster_whisper.tokenizer import Tokenizer
from faster_whisper.utils import download_model, format_timestamp, get_logger
from faster_whisper.vad import (
SpeechTimestampsMap,
VadOptions,
collect_chunks,
get_speech_timestamps,
)
@@ -67,6 +68,7 @@ class TranscriptionInfo(NamedTuple):
language_probability: float
duration: float
transcription_options: TranscriptionOptions
vad_options: VadOptions
class WhisperModel:
@@ -177,7 +179,7 @@ class WhisperModel:
prepend_punctuations: str = "\"'“¿([{-",
append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
vad_filter: bool = False,
vad_parameters: Optional[dict] = None,
vad_parameters: Optional[Union[dict, VadOptions]] = None,
) -> Tuple[Iterable[Segment], TranscriptionInfo]:
"""Transcribes an input file.
@@ -221,8 +223,8 @@ class WhisperModel:
vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
without speech. This step is using the Silero VAD model
https://github.com/snakers4/silero-vad.
vad_parameters: Dictionary of Silero VAD parameters (see available parameters and
default values in the function `get_speech_timestamps`).
vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
parameters and default values in the class `VadOptions`).
Returns:
A tuple with:
@@ -242,8 +244,11 @@ class WhisperModel:
)
if vad_filter:
vad_parameters = {} if vad_parameters is None else vad_parameters
speech_chunks = get_speech_timestamps(audio, **vad_parameters)
if vad_parameters is None:
vad_parameters = VadOptions()
elif isinstance(vad_parameters, dict):
vad_parameters = VadOptions(**vad_parameters)
speech_chunks = get_speech_timestamps(audio, vad_parameters)
audio = collect_chunks(audio, speech_chunks)
self.logger.info(
@@ -330,6 +335,7 @@ class WhisperModel:
language_probability=language_probability,
duration=duration,
transcription_options=options,
vad_options=vad_parameters,
)
return segments, info