Spaces:
Running
Running
File size: 8,762 Bytes
295de00 33ee1bb 295de00 8031785 295de00 adca588 295de00 74b7d77 295de00 5dcaadd 295de00 33ee1bb 295de00 33ee1bb 295de00 67b8308 4749691 67b8308 4749691 295de00 67b8308 295de00 74b7d77 8031785 295de00 74b7d77 295de00 74b7d77 295de00 8031785 74b7d77 8031785 295de00 74b7d77 295de00 a79dd83 295de00 33ee1bb a79dd83 33ee1bb 74b7d77 8031785 295de00 8031785 33ee1bb 295de00 33ee1bb 55b2bd6 5dcaadd 295de00 74b7d77 295de00 33ee1bb 295de00 adca588 295de00 adca588 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
import os
from typing import List, Union
from faster_whisper import WhisperModel, download_model
from src.config import ModelConfig, VadInitialPromptMode
from src.hooks.progressListener import ProgressListener
from src.languages import get_language_from_name
from src.modelCache import ModelCache
from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer
from src.utils import format_timestamp
class FasterWhisperContainer(AbstractWhisperContainer):
def __init__(self, model_name: str, device: str = None, compute_type: str = "float16",
download_root: str = None,
cache: ModelCache = None, models: List[ModelConfig] = []):
super().__init__(model_name, device, compute_type, download_root, cache, models)
def ensure_downloaded(self):
"""
Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before
passing the container to a subprocess.
"""
model_config = self._get_model_config()
if os.path.isdir(model_config.url):
model_config.path = model_config.url
else:
model_config.path = download_model(model_config.url, output_dir=self.download_root)
def _get_model_config(self) -> ModelConfig:
"""
Get the model configuration for the model.
"""
for model in self.models:
if model.name == self.model_name:
return model
return None
def _create_model(self):
print("Loading faster whisper model " + self.model_name + " for device " + str(self.device))
model_config = self._get_model_config()
model_url = model_config.url
if model_config.type == "whisper":
if model_url not in ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2", "large-v3"]:
raise Exception("FasterWhisperContainer does not yet support Whisper models. Use ct2-transformers-converter to convert the model to a faster-whisper model.")
if model_url == "large":
# large is an alias for large-v3
model_url = "large-v3"
device = self.device
if (device is None):
device = "auto"
model = WhisperModel(model_url, device=device, compute_type=self.compute_type)
return model
def create_callback(self, language: str = None, task: str = None,
prompt_strategy: AbstractPromptStrategy = None,
**decodeOptions: dict) -> AbstractWhisperCallback:
"""
Create a WhisperCallback object that can be used to transcript audio files.
Parameters
----------
language: str
The target language of the transcription. If not specified, the language will be inferred from the audio content.
task: str
The task - either translate or transcribe.
prompt_strategy: AbstractPromptStrategy
The prompt strategy to use. If not specified, the prompt from Whisper will be used.
decodeOptions: dict
Additional options to pass to the decoder. Must be pickleable.
Returns
-------
A WhisperCallback object.
"""
return FasterWhisperCallback(self, language=language, task=task, prompt_strategy=prompt_strategy, **decodeOptions)
class FasterWhisperCallback(AbstractWhisperCallback):
def __init__(self, model_container: FasterWhisperContainer, language: str = None, task: str = None,
prompt_strategy: AbstractPromptStrategy = None,
**decodeOptions: dict):
self.model_container = model_container
self.language = language
self.task = task
self.prompt_strategy = prompt_strategy
self.decodeOptions = decodeOptions
self._printed_warning = False
def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None):
"""
Peform the transcription of the given audio file or data.
Parameters
----------
audio: Union[str, np.ndarray, torch.Tensor]
The audio file to transcribe, or the audio data as a numpy array or torch tensor.
segment_index: int
The target language of the transcription. If not specified, the language will be inferred from the audio content.
task: str
The task - either translate or transcribe.
progress_listener: ProgressListener
A callback to receive progress updates.
"""
model: WhisperModel = self.model_container.get_model()
language_code = self._lookup_language_code(self.language) if self.language else None
# Copy decode options and remove options that are not supported by faster-whisper
decodeOptions = self.decodeOptions.copy()
verbose = decodeOptions.pop("verbose", None)
logprob_threshold = decodeOptions.pop("logprob_threshold", None)
patience = decodeOptions.pop("patience", None)
length_penalty = decodeOptions.pop("length_penalty", None)
suppress_tokens = decodeOptions.pop("suppress_tokens", None)
if (decodeOptions.pop("fp16", None) is not None):
if not self._printed_warning:
print("WARNING: fp16 option is ignored by faster-whisper - use compute_type instead.")
self._printed_warning = True
# Fix up decode options
if (logprob_threshold is not None):
decodeOptions["log_prob_threshold"] = logprob_threshold
decodeOptions["patience"] = float(patience) if patience is not None else 1.0
decodeOptions["length_penalty"] = float(length_penalty) if length_penalty is not None else 1.0
# See if supress_tokens is a string - if so, convert it to a list of ints
decodeOptions["suppress_tokens"] = self._split_suppress_tokens(suppress_tokens)
initial_prompt = self.prompt_strategy.get_segment_prompt(segment_index, prompt, detected_language) \
if self.prompt_strategy else prompt
segments_generator, info = model.transcribe(audio, \
language=language_code if language_code else detected_language, task=self.task, \
initial_prompt=initial_prompt, \
**decodeOptions
)
segments = []
for segment in segments_generator:
segments.append(segment)
if progress_listener is not None:
progress_listener.on_progress(segment.end, info.duration)
if verbose:
print("[{}->{}] {}".format(format_timestamp(segment.start, True), format_timestamp(segment.end, True),
segment.text))
text = " ".join([segment.text for segment in segments])
# Convert the segments to a format that is easier to serialize
whisper_segments = [{
"text": segment.text,
"start": segment.start,
"end": segment.end,
# Extra fields added by faster-whisper
"words": [{
"start": word.start,
"end": word.end,
"word": word.word,
"probability": word.probability
} for word in (segment.words if segment.words is not None else []) ]
} for segment in segments]
result = {
"segments": whisper_segments,
"text": text,
"language": info.language if info else None,
# Extra fields added by faster-whisper
"language_probability": info.language_probability if info else None,
"duration": info.duration if info else None
}
# If we have a prompt strategy, we need to increment the current prompt
if self.prompt_strategy:
self.prompt_strategy.on_segment_finished(segment_index, prompt, detected_language, result)
if progress_listener is not None:
progress_listener.on_finished()
return result
def _split_suppress_tokens(self, suppress_tokens: Union[str, List[int]]):
if (suppress_tokens is None):
return None
if (isinstance(suppress_tokens, list)):
return suppress_tokens
return [int(token) for token in suppress_tokens.split(",")]
def _lookup_language_code(self, language: str):
language = get_language_from_name(language)
if language is None:
raise ValueError("Invalid language: " + language)
return language.code
|