def transcribeAudio(self, utterances: list[Utterance], batch_size: Optional[int] = 1, decoder: Optional[Decoder] = None) -> list[dict]:
"""
Transcribe audio files.
Parameters:
----------
paths: list[Utterance]
List of audio utterances to transcribe
batch_size: Optional[int] = 1
Batch size to use for inference
decoder: Optional[Decoder] = None
Decoder to use for transcription. If you don't specify this, the engine will use the GreedyDecoder.
Returns:
----------
list[dict]:
A list of dictionaries containing the transcription for each audio file:
[{
"transcription": str,
"start_timesteps": list[int],
"end_timesteps": list[int],
"probabilities": list[float]
}, ...]
"""
if not self.is_finetuned:
raise ValueError("Not fine-tuned model! Please, fine-tune the model first.")
if decoder is None:
decoder = GreedyDecoder(self.token_set)
sampling_rate = self.processor.feature_extractor.sampling_rate
result = []
for utts_batch in tqdm(list(get_chunks(utterances, batch_size))):
#waveforms = get_waveforms(paths_batch, sampling_rate)
waveforms = []
for utt in utts_batch:
waveforms.append(utt.audio)
inputs = self.processor(waveforms, sampling_rate=sampling_rate, return_tensors="pt", padding=True, do_normalize=True)
with torch.no_grad():
logits = self.model(inputs.input_values.to(self.device), attention_mask=inputs.attention_mask.to(self.device)).logits
batchResults = decoder(logits)
for index, br in enumerate(batchResults):
if(len(br['transcription'].strip()) == 0):
continue;
br['utterance_start'] = '%.2f' % utts_batch[index].timestamp
br['utterance_duration'] = '%.2f' % utts_batch[index].duration
br['tokens'] = self.convertToCTM(br)
result.append(br)
return result
def transcribeFiles(self,
paths: list[str],
batch_size: Optional[int] = 1,
decoder: Optional[Decoder] = None) -> list[dict]:
"""
Transcribe audio files.
Parameters:
----------
paths: list[str]
List of paths to audio files to transcribe
batch_size: Optional[int] = 1
Batch size to use for inference
decoder: Optional[Decoder] = None
Decoder to use for transcription. If you don't specify this, the engine will use the GreedyDecoder.
Returns:
----------
list[dict]:
A list of dictionaries containing the transcription for each audio file:
[{
"transcription": str,
"start_timesteps": list[int],
"end_timesteps": list[int],
"probabilities": list[float]
}, ...]
"""
if not self.is_finetuned:
raise ValueError(
"Not fine-tuned model! Please, fine-tune the model first.")
if decoder is None:
decoder = GreedyDecoder(self.token_set)
sampling_rate = self.processor.feature_extractor.sampling_rate
result = []
for paths_batch in tqdm(list(get_chunks(paths, batch_size))):
waveforms = get_waveforms(paths_batch, sampling_rate)
inputs = self.processor(waveforms,
sampling_rate=sampling_rate,
return_tensors="pt",
padding=True,
do_normalize=True)
with torch.no_grad():
logits = self.model(inputs.input_values.to(self.device),
attention_mask=inputs.attention_mask.to(
self.device)).logits
result += decoder(logits)
return result