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| import whisper | |
| import gradio as gr | |
| import time | |
| import os | |
| from typing import BinaryIO, Union, Tuple, List | |
| import numpy as np | |
| import torch | |
| from modules.whisper_base import WhisperBase | |
| from modules.whisper_parameter import * | |
| class WhisperInference(WhisperBase): | |
| def __init__(self): | |
| super().__init__( | |
| model_dir=os.path.join("models", "Whisper") | |
| ) | |
| 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 | |
| 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 | |
| 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, | |
| ): | |
| """ | |
| 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 | |
| ) |