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Added download path parameter.

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prameshbajra
2023-05-27 11:38:54 +02:00
parent f1032bb40a
commit 5a47f458ac
1 changed files with 24 additions and 11 deletions
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35
whisperx/asr.py
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@ -13,8 +13,16 @@ from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
from .vad import load_vad_model, merge_chunks from .vad import load_vad_model, merge_chunks
from .types import TranscriptionResult, SingleSegment from .types import TranscriptionResult, SingleSegment
def load_model(whisper_arch, device, device_index=0, compute_type="float16", asr_options=None, language=None, def load_model(whisper_arch,
vad_options=None, model=None, task="transcribe"): device,
device_index=0,
compute_type="float16",
asr_options=None,
language=None,
vad_options=None,
model=None,
task="transcribe",
download_root=None):
'''Load a Whisper model for inference. '''Load a Whisper model for inference.
Args: Args:
whisper_arch: str - The name of the Whisper model to load. whisper_arch: str - The name of the Whisper model to load.
@ -22,14 +30,19 @@ def load_model(whisper_arch, device, device_index=0, compute_type="float16", asr
compute_type: str - The compute type to use for the model. compute_type: str - The compute type to use for the model.
options: dict - A dictionary of options to use for the model. options: dict - A dictionary of options to use for the model.
language: str - The language of the model. (use English for now) language: str - The language of the model. (use English for now)
download_root: Optional[str] - The root directory to download the model to.
Returns: Returns:
A Whisper pipeline. A Whisper pipeline.
''' '''
if whisper_arch.endswith(".en"): if whisper_arch.endswith(".en"):
language = "en" language = "en"
model = WhisperModel(whisper_arch, device=device, device_index=device_index, compute_type=compute_type) model = WhisperModel(whisper_arch,
device=device,
device_index=device_index,
compute_type=compute_type,
download_root=download_root)
if language is not None: if language is not None:
tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language) tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
else: else:
@ -114,7 +127,7 @@ class WhisperModel(faster_whisper.WhisperModel):
# suppress_tokens=options.suppress_tokens, # suppress_tokens=options.suppress_tokens,
# max_initial_timestamp_index=max_initial_timestamp_index, # max_initial_timestamp_index=max_initial_timestamp_index,
) )
tokens_batch = [x.sequences_ids[0] for x in result] tokens_batch = [x.sequences_ids[0] for x in result]
def decode_batch(tokens: List[List[int]]) -> str: def decode_batch(tokens: List[List[int]]) -> str:
@ -127,7 +140,7 @@ class WhisperModel(faster_whisper.WhisperModel):
text = decode_batch(tokens_batch) text = decode_batch(tokens_batch)
return text return text
def encode(self, features: np.ndarray) -> ctranslate2.StorageView: def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
# When the model is running on multiple GPUs, the encoder output should be moved # 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 the CPU since we don't know which GPU will handle the next job.
@ -136,9 +149,9 @@ class WhisperModel(faster_whisper.WhisperModel):
if len(features.shape) == 2: if len(features.shape) == 2:
features = np.expand_dims(features, 0) features = np.expand_dims(features, 0)
features = faster_whisper.transcribe.get_ctranslate2_storage(features) features = faster_whisper.transcribe.get_ctranslate2_storage(features)
return self.model.encode(features, to_cpu=to_cpu) return self.model.encode(features, to_cpu=to_cpu)
class FasterWhisperPipeline(Pipeline): class FasterWhisperPipeline(Pipeline):
""" """
Huggingface Pipeline wrapper for FasterWhisperModel. Huggingface Pipeline wrapper for FasterWhisperModel.
@ -176,7 +189,7 @@ class FasterWhisperPipeline(Pipeline):
self.device = torch.device(f"cuda:{device}") self.device = torch.device(f"cuda:{device}")
else: else:
self.device = device self.device = device
super(Pipeline, self).__init__() super(Pipeline, self).__init__()
self.vad_model = vad self.vad_model = vad
@ -194,7 +207,7 @@ class FasterWhisperPipeline(Pipeline):
def _forward(self, model_inputs): def _forward(self, model_inputs):
outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options) outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options)
return {'text': outputs} return {'text': outputs}
def postprocess(self, model_outputs): def postprocess(self, model_outputs):
return model_outputs return model_outputs
@ -218,7 +231,7 @@ class FasterWhisperPipeline(Pipeline):
) -> TranscriptionResult: ) -> TranscriptionResult:
if isinstance(audio, str): if isinstance(audio, str):
audio = load_audio(audio) audio = load_audio(audio)
def data(audio, segments): def data(audio, segments):
for seg in segments: for seg in segments:
f1 = int(seg['start'] * SAMPLE_RATE) f1 = int(seg['start'] * SAMPLE_RATE)