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import gradio as gr | |
#import whisper | |
from faster_whisper import WhisperModel | |
model_size = 'aka7774/whisper-large-v3-ct2' | |
#model = whisper.load_model(model_size) | |
#model = WhisperModel(model_size, device="cuda", compute_type="float16") | |
# or run on GPU with INT8 | |
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16") | |
# or run on CPU with INT8 | |
model = WhisperModel(model_size, device="cpu", compute_type="int8") | |
def speech_to_text(audio_file, _model_size): | |
global model_size, model | |
if model_size != _model_size: | |
model_size = _model_size | |
#model = whisper.load_model(model_size) | |
model = WhisperModel(model_size, compute_type="float16") | |
#result = model.transcribe(audio_file) | |
segments, info = model.transcribe(audio_file, beam_size=5) | |
#return result["text"] | |
return "".join([segment.text for segment in segments]) | |
gr.Interface( | |
fn=speech_to_text, | |
inputs=[ | |
gr.Audio(source="upload", type="filepath"), | |
gr.Dropdown(value=model_size, choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3", "aka7774/whisper-large-v3-ct2"]), | |
], | |
outputs="text").launch() | |