Spaces:
Running
Running
File size: 6,814 Bytes
8dac861 f7b7cdd 8dac861 f7b7cdd 8dac861 f7b7cdd 6d61e4e 51f672d 48dc726 51f672d 25f2bab 8dac861 1c725ac 8a27aeb 48dc726 1c725ac 8dac861 8a27aeb 48dc726 8dac861 6d61e4e f7b7cdd 8dac861 6fb5189 8dac861 227c267 8dac861 51f672d 171d562 51f672d 8dac861 afeb40e 8dac861 afeb40e 8dac861 f7b7cdd 8dac861 f7b7cdd afeb40e f7b7cdd 51f672d f7b7cdd afeb40e 6d61e4e afeb40e 6d61e4e |
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 |
import os
import time
import numpy as np
from typing import BinaryIO, Union, Tuple, List
import torch
from transformers import pipeline
from transformers.utils import is_flash_attn_2_available
import gradio as gr
from huggingface_hub import hf_hub_download
import whisper
from rich.progress import Progress, TimeElapsedColumn, BarColumn, TextColumn
from argparse import Namespace
from modules.whisper.whisper_parameter import *
from modules.whisper.whisper_base import WhisperBase
class InsanelyFastWhisperInference(WhisperBase):
def __init__(self,
model_dir: str,
output_dir: str,
args: Namespace
):
super().__init__(
model_dir=model_dir,
output_dir=output_dir,
args=args
)
openai_models = whisper.available_models()
distil_models = ["distil-large-v2", "distil-large-v3", "distil-medium.en", "distil-small.en"]
self.available_models = openai_models + distil_models
self.available_compute_types = ["float16"]
def transcribe(self,
audio: Union[str, np.ndarray, torch.Tensor],
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
Parameters related with whisper. This will be dealt with "WhisperParameters" data class
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 = WhisperParameters.as_value(*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]
progress(0, desc="Transcribing...Progress is not shown in insanely-fast-whisper.")
with Progress(
TextColumn("[progress.description]{task.description}"),
BarColumn(style="yellow1", pulse_style="white"),
TimeElapsedColumn(),
) as progress:
progress.add_task("[yellow]Transcribing...", total=None)
segments = self.model(
inputs=audio,
return_timestamps=True,
chunk_length_s=params.chunk_length_s,
batch_size=params.batch_size,
generate_kwargs={
"language": params.lang,
"task": "translate" if params.is_translate and self.current_model_size in self.translatable_models else "transcribe",
"no_speech_threshold": params.no_speech_threshold,
"temperature": params.temperature,
"compression_ratio_threshold": params.compression_ratio_threshold
}
)
segments_result = self.format_result(
transcribed_result=segments,
)
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..")
model_path = os.path.join(self.model_dir, model_size)
if not os.path.isdir(model_path) or not os.listdir(model_path):
self.download_model(
model_size=model_size,
download_root=model_path,
progress=progress
)
self.current_compute_type = compute_type
self.current_model_size = model_size
self.model = pipeline(
"automatic-speech-recognition",
model=os.path.join(self.model_dir, model_size),
torch_dtype=self.current_compute_type,
device=self.device,
model_kwargs={"attn_implementation": "flash_attention_2"} if is_flash_attn_2_available() else {"attn_implementation": "sdpa"},
)
@staticmethod
def format_result(
transcribed_result: dict
) -> List[dict]:
"""
Format the transcription result of insanely_fast_whisper as the same with other implementation.
Parameters
----------
transcribed_result: dict
Transcription result of the insanely_fast_whisper
Returns
----------
result: List[dict]
Formatted result as the same with other implementation
"""
result = transcribed_result["chunks"]
for item in result:
start, end = item["timestamp"][0], item["timestamp"][1]
if end is None:
end = start
item["start"] = start
item["end"] = end
return result
@staticmethod
def download_model(
model_size: str,
download_root: str,
progress: gr.Progress
):
progress(0, 'Initializing model..')
print(f'Downloading {model_size} to "{download_root}"....')
os.makedirs(download_root, exist_ok=True)
download_list = [
"model.safetensors",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"tokenizer.json",
"tokenizer_config.json",
"added_tokens.json",
"special_tokens_map.json",
"vocab.json",
]
if model_size.startswith("distil"):
repo_id = f"distil-whisper/{model_size}"
else:
repo_id = f"openai/whisper-{model_size}"
for item in download_list:
hf_hub_download(repo_id=repo_id, filename=item, local_dir=download_root)
|