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asr.py
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asr.py
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import os
import warnings
from typing import List, Union, Optional, NamedTuple
import ctranslate2
import faster_whisper
import numpy as np
import torch
from transformers import Pipeline
from transformers.pipelines.pt_utils import PipelineIterator
from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
from .vad import load_vad_model, merge_chunks
from .types import TranscriptionResult, SingleSegment
def find_numeral_symbol_tokens(tokenizer):
numeral_symbol_tokens = []
for i in range(tokenizer.eot):
token = tokenizer.decode([i]).removeprefix(" ")
has_numeral_symbol = any(c in "0123456789%$£" for c in token)
if has_numeral_symbol:
numeral_symbol_tokens.append(i)
return numeral_symbol_tokens
class WhisperModel(faster_whisper.WhisperModel):
'''
FasterWhisperModel provides batched inference for faster-whisper.
Currently only works in non-timestamp mode and fixed prompt for all samples in batch.
'''
def generate_segment_batched(self, features: np.ndarray, tokenizer: faster_whisper.tokenizer.Tokenizer, options: faster_whisper.transcribe.TranscriptionOptions, encoder_output = None):
batch_size = features.shape[0]
all_tokens = []
prompt_reset_since = 0
if options.initial_prompt is not None:
initial_prompt = " " + options.initial_prompt.strip()
initial_prompt_tokens = tokenizer.encode(initial_prompt)
all_tokens.extend(initial_prompt_tokens)
previous_tokens = all_tokens[prompt_reset_since:]
prompt = self.get_prompt(
tokenizer,
previous_tokens,
without_timestamps=options.without_timestamps,
prefix=options.prefix,
)
encoder_output = self.encode(features)
max_initial_timestamp_index = int(
round(options.max_initial_timestamp / self.time_precision)
)
result = self.model.generate(
encoder_output,
[prompt] * batch_size,
beam_size=options.beam_size,
patience=options.patience,
length_penalty=options.length_penalty,
max_length=self.max_length,
suppress_blank=options.suppress_blank,
suppress_tokens=options.suppress_tokens,
)
tokens_batch = [x.sequences_ids[0] for x in result]
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)
return self.model.encode(features, to_cpu=to_cpu)
class FasterWhisperPipeline(Pipeline):
"""
Huggingface Pipeline wrapper for FasterWhisperModel.
"""
# TODO:
# - add support for timestamp mode
# - add support for custom inference kwargs
def __init__(
self,
model,
vad,
vad_params: dict,
options : NamedTuple,
tokenizer=None,
device: Union[int, str, "torch.device"] = -1,
framework = "pt",
language : Optional[str] = None,
suppress_numerals: bool = False,
**kwargs
):
self.model = model
self.tokenizer = tokenizer
self.options = options
self.preset_language = language
self.suppress_numerals = suppress_numerals
self._batch_size = kwargs.pop("batch_size", None)
self._num_workers = 1
self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs)
self.call_count = 0
self.framework = framework
if self.framework == "pt":
if isinstance(device, torch.device):
self.device = device
elif isinstance(device, str):
self.device = torch.device(device)
elif device < 0:
self.device = torch.device("cpu")
else:
self.device = torch.device(f"cuda:{device}")
else:
self.device = device
super(Pipeline, self).__init__()
self.vad_model = vad
self._vad_params = vad_params
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "tokenizer" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
return preprocess_kwargs, {}, {}
def preprocess(self, audio):
audio = audio['inputs']
model_n_mels = self.model.feat_kwargs.get("feature_size")
features = log_mel_spectrogram(
audio,
n_mels=model_n_mels if model_n_mels is not None else 80,
padding=N_SAMPLES - audio.shape[0],
)
return {'inputs': features}
def _forward(self, model_inputs):
outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options)
return {'text': outputs}
def postprocess(self, model_outputs):
return model_outputs
def get_iterator(
self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
):
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])}
dataloader = torch.utils.data.DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=stack)
model_iterator = PipelineIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
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
) -> TranscriptionResult:
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})
vad_segments = merge_chunks(
vad_segments,
chunk_size,
onset=self._vad_params["vad_onset"],
offset=self._vad_params["vad_offset"],
)
if self.tokenizer is None:
language = language or self.detect_language(audio)
task = task or "transcribe"
self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
self.model.model.is_multilingual, task=task,
language=language)
else:
language = language or self.tokenizer.language_code
task = task or self.tokenizer.task
if task != self.tokenizer.task or language != self.tokenizer.language_code:
self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
self.model.model.is_multilingual, task=task,
language=language)
if self.suppress_numerals:
previous_suppress_tokens = self.options.suppress_tokens
numeral_symbol_tokens = find_numeral_symbol_tokens(self.tokenizer)
print(f"Suppressing numeral and symbol tokens")
new_suppressed_tokens = numeral_symbol_tokens + self.options.suppress_tokens
new_suppressed_tokens = list(set(new_suppressed_tokens))
self.options = self.options._replace(suppress_tokens=new_suppressed_tokens)
segments: List[SingleSegment] = []
batch_size = batch_size or self._batch_size
total_segments = len(vad_segments)
for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
if print_progress:
base_progress = ((idx + 1) / total_segments) * 100
percent_complete = base_progress / 2 if combined_progress else base_progress
print(f"Progress: {percent_complete:.2f}%...")
text = out['text']
if batch_size in [0, 1, None]:
text = text[0]
segments.append(
{
"text": text,
"start": round(vad_segments[idx]['start'], 3),
"end": round(vad_segments[idx]['end'], 3)
}
)
# 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.")
model_n_mels = self.model.feat_kwargs.get("feature_size")
segment = log_mel_spectrogram(audio[: N_SAMPLES],
n_mels=model_n_mels if model_n_mels is not None else 80,
padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
encoder_output = self.model.encode(segment)
results = self.model.model.detect_language(encoder_output)
language_token, language_probability = results[0][0]
language = language_token[2:-2]
print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
return language
def load_model(whisper_arch,
device,
device_index=0,
compute_type="float16",
asr_options=None,
language : Optional[str] = None,
vad_model=None,
vad_options=None,
model : Optional[WhisperModel] = None,
task="transcribe",
download_root=None,
threads=4):
'''Load a Whisper model for inference.
Args:
whisper_arch: str - The name of the Whisper model to load.
device: str - The device to load the model on.
compute_type: str - The compute type 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)
model: Optional[WhisperModel] - The WhisperModel instance to use.
download_root: Optional[str] - The root directory to download the model to.
threads: int - The number of cpu threads to use per worker, e.g. will be multiplied by num workers.
Returns:
A Whisper pipeline.
'''
if whisper_arch.endswith(".en"):
language = "en"
model = model or WhisperModel(whisper_arch,
device=device,
device_index=device_index,
compute_type=compute_type,
download_root=download_root,
cpu_threads=threads)
if language is not None:
tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
else:
print("No language specified, language will be first be detected for each audio file (increases inference time).")
tokenizer = None
default_asr_options = {
"beam_size": 5,
"best_of": 5,
"patience": 1,
"length_penalty": 1,
"repetition_penalty": 1,
"no_repeat_ngram_size": 0,
"temperatures": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0],
"compression_ratio_threshold": 2.4,
"log_prob_threshold": -1.0,
"no_speech_threshold": 0.6,
"condition_on_previous_text": False,
"prompt_reset_on_temperature": 0.5,
"initial_prompt": None,
"prefix": None,
"suppress_blank": True,
"suppress_tokens": [-1],
"without_timestamps": True,
"max_initial_timestamp": 0.0,
"word_timestamps": False,
"prepend_punctuations": "\"'“¿([{-",
"append_punctuations": "\"'.。,,!!??::”)]}、",
"suppress_numerals": False,
"max_new_tokens": None,
"clip_timestamps": None,
"hallucination_silence_threshold": None,
}
if asr_options is not None:
default_asr_options.update(asr_options)
suppress_numerals = default_asr_options["suppress_numerals"]
del default_asr_options["suppress_numerals"]
default_asr_options = faster_whisper.transcribe.TranscriptionOptions(**default_asr_options)
default_vad_options = {
"vad_onset": 0.500,
"vad_offset": 0.363
}
if vad_options is not None:
default_vad_options.update(vad_options)
if vad_model is not None:
vad_model = vad_model
else:
vad_model = load_vad_model(torch.device(device), use_auth_token=None, **default_vad_options)
return FasterWhisperPipeline(
model=model,
vad=vad_model,
options=default_asr_options,
tokenizer=tokenizer,
language=language,
suppress_numerals=suppress_numerals,
vad_params=default_vad_options,
)