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feat: Enable optional dynamic prompting for the FasterWhisperPipeline #913

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feat: Enable optional dynamic prompting for the FasterWhisperPipeline #913

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16 changes: 5 additions & 11 deletions whisperx/asr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,18 +67,14 @@ def decode_batch(tokens: List[List[int]]) -> str:
res = []
for tk in tokens:
res.append([token for token in tk if token < tokenizer.eot])
# text_tokens = [token for token in tokens if token < self.eot]
return tokenizer.tokenizer.decode_batch(res)

text = decode_batch(tokens_batch)

return text

def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
# When the model is running on multiple GPUs, the encoder output should be moved
# to the CPU since we don't know which GPU will handle the next job.
to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
# unsqueeze if batch size = 1
if len(features.shape) == 2:
features = np.expand_dims(features, 0)
features = faster_whisper.transcribe.get_ctranslate2_storage(features)
Expand Down Expand Up @@ -161,7 +157,6 @@ def get_iterator(
dataset = PipelineIterator(inputs, self.preprocess, preprocess_params)
if "TOKENIZERS_PARALLELISM" not in os.environ:
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# TODO hack by collating feature_extractor and image_processor

def stack(items):
return {'inputs': torch.stack([x['inputs'] for x in items])}
Expand All @@ -171,16 +166,18 @@ def stack(items):
return final_iterator

def transcribe(
self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False
self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False, prompt=None
) -> TranscriptionResult:
if prompt:
self.options = self.options._replace(initial_prompt=prompt)

if isinstance(audio, str):
audio = load_audio(audio)

def data(audio, segments):
for seg in segments:
f1 = int(seg['start'] * SAMPLE_RATE)
f2 = int(seg['end'] * SAMPLE_RATE)
# print(f2-f1)
yield {'inputs': audio[f1:f2]}

vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
Expand Down Expand Up @@ -231,17 +228,14 @@ def data(audio, segments):
}
)

# revert the tokenizer if multilingual inference is enabled
if self.preset_language is None:
self.tokenizer = None

# revert suppressed tokens if suppress_numerals is enabled
if self.suppress_numerals:
self.options = self.options._replace(suppress_tokens=previous_suppress_tokens)

return {"segments": segments, "language": language}


def detect_language(self, audio: np.ndarray):
if audio.shape[0] < N_SAMPLES:
print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
Expand Down Expand Up @@ -354,4 +348,4 @@ def load_model(whisper_arch,
language=language,
suppress_numerals=suppress_numerals,
vad_params=default_vad_options,
)
)