Spaces:
Running
Running
File size: 5,649 Bytes
db2039e cdb03d3 db2039e bbb23df 9679f1e db2039e 11a447c c89be6a db2039e dcca39f db2039e c89be6a db2039e c89be6a c8ea3a4 b065a65 db2039e b065a65 cdb03d3 db2039e b065a65 db2039e 13698da db2039e 61ac4a7 db2039e 83f9929 ca9094f b065a65 752e22b 83f9929 f4609a6 752e22b b065a65 bbb23df db2039e 752e22b b065a65 e3a6426 3a1a0a3 3aeef88 f5deb64 bbb23df db2039e a5dbf21 db2039e b065a65 db2039e 22a07bc db2039e b065a65 c8ea3a4 b065a65 c8ea3a4 25c9e51 b065a65 db2039e c8ea3a4 |
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 |
import os
import time
import numpy as np
from typing import BinaryIO, Union, Tuple, List
import faster_whisper
from faster_whisper.vad import VadOptions
import ctranslate2
import whisper
import gradio as gr
from modules.whisper_parameter import *
from modules.whisper_base import WhisperBase
# Temporal fix of the issue : https://github.com/jhj0517/Whisper-WebUI/issues/144
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
class FasterWhisperInference(WhisperBase):
def __init__(self):
super().__init__(
model_dir=os.path.join("models", "Whisper", "faster-whisper")
)
self.model_paths = self.get_model_paths()
self.available_models = self.model_paths.keys()
self.available_compute_types = ctranslate2.get_supported_compute_types(
"cuda") if self.device == "cuda" else ctranslate2.get_supported_compute_types("cpu")
def transcribe(self,
audio: Union[str, BinaryIO, np.ndarray],
progress: gr.Progress,
*whisper_params,
) -> Tuple[List[dict], float]:
"""
transcribe method for faster-whisper.
Parameters
----------
audio: Union[str, BinaryIO, np.ndarray]
Audio path or file binary or Audio numpy array
progress: gr.Progress
Indicator to show progress directly in gradio.
*whisper_params: tuple
Gradio components related to Whisper. see whisper_data_class.py for details.
Returns
----------
segments_result: List[dict]
list of dicts that includes start, end timestamps and transcribed text
elapsed_time: float
elapsed time for transcription
"""
start_time = time.time()
params = WhisperValues(*whisper_params)
if params.model_size != self.current_model_size or self.model is None or self.current_compute_type != params.compute_type:
self.update_model(params.model_size, params.compute_type, progress)
if params.lang == "Automatic Detection":
params.lang = None
else:
language_code_dict = {value: key for key, value in whisper.tokenizer.LANGUAGES.items()}
params.lang = language_code_dict[params.lang]
vad_options = VadOptions(
threshold=params.threshold,
min_speech_duration_ms=params.min_speech_duration_ms,
max_speech_duration_s=params.max_speech_duration_s,
min_silence_duration_ms=params.min_silence_duration_ms,
window_size_samples=params.window_size_samples,
speech_pad_ms=params.speech_pad_ms
)
segments, info = self.model.transcribe(
audio=audio,
language=params.lang,
task="translate" if params.is_translate and self.current_model_size in self.translatable_models else "transcribe",
beam_size=params.beam_size,
log_prob_threshold=params.log_prob_threshold,
no_speech_threshold=params.no_speech_threshold,
best_of=params.best_of,
patience=params.patience,
temperature=params.temperature,
compression_ratio_threshold=params.compression_ratio_threshold,
vad_filter=params.vad_filter,
vad_parameters=vad_options
)
progress(0, desc="Loading audio..")
segments_result = []
for segment in segments:
progress(segment.start / info.duration, desc="Transcribing..")
segments_result.append({
"start": segment.start,
"end": segment.end,
"text": segment.text
})
elapsed_time = time.time() - start_time
return segments_result, elapsed_time
def update_model(self,
model_size: str,
compute_type: str,
progress: gr.Progress
):
"""
Update current model setting
Parameters
----------
model_size: str
Size of whisper model
compute_type: str
Compute type for transcription.
see more info : https://opennmt.net/CTranslate2/quantization.html
progress: gr.Progress
Indicator to show progress directly in gradio.
"""
progress(0, desc="Initializing Model..")
self.current_model_size = self.model_paths[model_size]
self.current_compute_type = compute_type
self.model = faster_whisper.WhisperModel(
device=self.device,
model_size_or_path=self.current_model_size,
download_root=self.model_dir,
compute_type=self.current_compute_type
)
def get_model_paths(self):
"""
Get available models from models path including fine-tuned model.
Returns
----------
Name list of models
"""
model_paths = {model:model for model in whisper.available_models()}
faster_whisper_prefix = "models--Systran--faster-whisper-"
existing_models = os.listdir(self.model_dir)
wrong_dirs = [".locks"]
existing_models = list(set(existing_models) - set(wrong_dirs))
webui_dir = os.getcwd()
for model_name in existing_models:
if faster_whisper_prefix in model_name:
model_name = model_name[len(faster_whisper_prefix):]
if model_name not in whisper.available_models():
model_paths[model_name] = os.path.join(webui_dir, self.model_dir, model_name)
return model_paths
|