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| from transformers import pipeline | |
| import os | |
| import gradio as gr | |
| import torch | |
| #from IPython.display import Audio as IPythonAudio | |
| #from gtts import gTTS | |
| #import IPython.display as ipd | |
| #Audio to text | |
| #asr = pipeline(task="automatic-speech-recognition", | |
| # model="distil-whisper/distil-small.en") | |
| #Text to text | |
| translator = pipeline(task="translation", | |
| model="facebook/nllb-200-distilled-600M", | |
| torch_dtype=torch.bfloat16) | |
| #Text to audio | |
| pipe = pipeline("text-to-speech", model="suno/bark-small") | |
| demo = gr.Blocks() | |
| def transcribe_speech(filepath): | |
| if filepath is None: | |
| gr.Warning("No audio found, please retry.") | |
| return "" | |
| #asr(filepath)["text"] | |
| output = translator(filepath, | |
| src_lang="eng_Latn", | |
| tgt_lang="hin_Deva") | |
| narrated_text=pipe(output[0]['translation_text']) | |
| #tts = gTTS(text=narrated_text, lang='hi', slow=False) | |
| #tts.save("translated_audio.mp3") | |
| #return ipd.Audio("translated_audio.mp3", autoplay=True) | |
| return narrated_text | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| #inputs=gr.Audio(sources="microphone", | |
| # type="filepath"), | |
| inputs=gr.Textbox(label="text",lines=3), | |
| outputs="audio", | |
| #outputs=gr.Audio(label="Translated Message"), | |
| allow_flagging="never") | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| #inputs=gr.Audio(sources="upload", | |
| # type="filepath"), | |
| inputs=gr.Textbox(label="text",lines=3), | |
| outputs="audio", | |
| #outputs=gr.Audio(label="Translated Message"), | |
| allow_flagging="never" | |
| ) | |
| with demo: | |
| gr.TabbedInterface( | |
| [mic_transcribe, | |
| file_transcribe], | |
| ["Transcribe Microphone", | |
| "Transcribe Audio File"], | |
| ) | |
| demo.launch(share=True) | |
| demo.close() |