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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,
model_dir: str,
output_dir: str
):
super().__init__(
model_dir=model_dir,
output_dir=output_dir
)
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 = WhisperParameters.post_process(*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
) |