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| import whisper | |
| import gradio as gr | |
| import time | |
| model = whisper.load_model("base") | |
| #from transformers import pipeline | |
| #es_en_translator = pipeline("translation_es_to_en") | |
| def transcribe(audio): | |
| #time.sleep(3) | |
| # load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| # make log-Mel spectrogram and move to the same device as the model | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| # detect the spoken language | |
| _, probs = model.detect_language(mel) | |
| print(f"Detected language: {max(probs, key=probs.get)}") | |
| #lang = LANGUAGES[language] | |
| #lang=(f"Detected language: {lang}") | |
| # decode the audio | |
| options = whisper.DecodingOptions(fp16 = False)#,task= "translate") | |
| result = whisper.decode(model, mel, options) | |
| #word= result.text | |
| #trans = es_en_translator(word) | |
| #Trans = trans[0]['translation_text'] | |
| #result=f"{lang}\n{word}\n\nEnglish translation: {Trans}" | |
| return result.text | |
| gr.Interface( | |
| title = 'SPEECH TO TEXT', | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath") | |
| ], | |
| outputs=[ | |
| "textbox" | |
| ], | |
| live=True).launch() |