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Merge branch 'master' into prompt

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黑墨水鱼
2024-04-08 20:56:49 +08:00
parent e50d82c18c 91c8307aa6
commit 4ee1d54c14
7 changed files with 78 additions and 28 deletions
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62
faster_whisper/transcribe.py
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@@ -220,6 +220,8 @@ class WhisperModel:
chunk_length: Optional[int] = None,
clip_timestamps: Union[str, List[float]] = "0",
hallucination_silence_threshold: Optional[float] = None,
language_detection_threshold: Optional[float] = None,
language_detection_segments: int = 1,
) -> Tuple[Iterable[Segment], TranscriptionInfo]:
"""Transcribes an input file.
@@ -278,9 +280,13 @@ class WhisperModel:
clip_timestamps: Union[str, List[float]]
Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to
process. The last end timestamp defaults to the end of the file.
vad_filter will be ignored if clip_timestamps is used.
hallucination_silence_threshold: Optional[float]
When word_timestamps is True, skip silent periods longer than this threshold
(in seconds) when a possible hallucination is detected
language_detection_threshold: If the maximum probability of the language tokens is higher
than this value, the language is detected.
language_detection_segments: Number of segments to consider for the language detection.
Returns:
A tuple with:
@@ -300,7 +306,7 @@ class WhisperModel:
"Processing audio with duration %s", format_timestamp(duration)
)
if vad_filter:
if vad_filter and clip_timestamps == "0":
if vad_parameters is None:
vad_parameters = VadOptions()
elif isinstance(vad_parameters, dict):
@@ -340,15 +346,51 @@ class WhisperModel:
language = "en"
language_probability = 1
else:
segment = features[:, : self.feature_extractor.nb_max_frames]
encoder_output = self.encode(segment)
# results is a list of tuple[str, float] with language names and
# probabilities.
results = self.model.detect_language(encoder_output)[0]
# Parse language names to strip out markers
all_language_probs = [(token[2:-2], prob) for (token, prob) in results]
# Get top language token and probability
language, language_probability = all_language_probs[0]
if (
language_detection_segments is None
or language_detection_segments < 1
):
language_detection_segments = 1
seek = 0
detected_language_info = {}
content_frames = (
features.shape[-1] - self.feature_extractor.nb_max_frames
)
while (
seek <= content_frames
and seek
< self.feature_extractor.nb_max_frames * language_detection_segments
):
segment = features[
:, seek : seek + self.feature_extractor.nb_max_frames
]
encoder_output = self.encode(segment)
# results is a list of tuple[str, float] with language names and
# probabilities.
results = self.model.detect_language(encoder_output)[0]
# Parse language names to strip out markers
all_language_probs = [
(token[2:-2], prob) for (token, prob) in results
]
# Get top language token and probability
language, language_probability = all_language_probs[0]
if (
language_detection_threshold is None
or language_probability > language_detection_threshold
):
break
detected_language_info.setdefault(language, []).append(
language_probability
)
seek += segment.shape[-1]
else:
# If no language detected for all segments, the majority vote of the highest
# projected languages for all segments is used to determine the language.
language = max(
detected_language_info,
key=lambda lang: len(detected_language_info[lang]),
)
language_probability = max(detected_language_info[language])
self.logger.info(
"Detected language '%s' with probability %.2f",