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import whisper
import gradio as gr
import time
from typing import BinaryIO, Union, Tuple, List
import numpy as np
import torch
import os
from argparse import Namespace
from modules.utils.paths import (WHISPER_MODELS_DIR, DIARIZATION_MODELS_DIR, OUTPUT_DIR, UVR_MODELS_DIR)
from modules.whisper.whisper_base import WhisperBase
from modules.whisper.whisper_parameter import *
class WhisperInference(WhisperBase):
def __init__(self,
model_dir: str = WHISPER_MODELS_DIR,
diarization_model_dir: str = DIARIZATION_MODELS_DIR,
uvr_model_dir: str = UVR_MODELS_DIR,
output_dir: str = OUTPUT_DIR,
):
super().__init__(
model_dir=model_dir,
output_dir=output_dir,
diarization_model_dir=diarization_model_dir,
uvr_model_dir=uvr_model_dir
)
def transcribe(self,
audio: Union[str, np.ndarray, torch.Tensor],
progress: gr.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)
def progress_callback(progress_value):
progress(progress_value, desc="Transcribing..")
segments_result = self.model.transcribe(audio=audio,
language=params.lang,
verbose=False,
beam_size=params.beam_size,
logprob_threshold=params.log_prob_threshold,
no_speech_threshold=params.no_speech_threshold,
task="translate" if params.is_translate and self.current_model_size in self.translatable_models else "transcribe",
fp16=True if params.compute_type == "float16" else False,
best_of=params.best_of,
patience=params.patience,
temperature=params.temperature,
compression_ratio_threshold=params.compression_ratio_threshold,
progress_callback=progress_callback,)["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 = 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_compute_type = compute_type
self.current_model_size = model_size
self.model = whisper.load_model(
name=model_size,
device=self.device,
download_root=self.model_dir
) |