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"""ML_task3.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1DfK6fjkAd9RjVx3MUGfDtAOulvEenk0E |
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""" |
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!pip install gradio |
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!pip install datasets |
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!pip install transformers |
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import gradio as gr |
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import numpy as np |
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import torch |
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from datasets import load_dataset |
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from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline, WhisperProcessor |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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asr_pipe = pipeline("automatic-speech-recognition", model="voidful/wav2vec2-xlsr-multilingual-56", device=device) |
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!pip -q install sentencepiece |
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processor = WhisperProcessor.from_pretrained( |
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"openai/whisper-small") |
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translator_en = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en") |
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translator_ru = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru") |
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from transformers import VitsModel, VitsTokenizer |
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model = VitsModel.from_pretrained("facebook/mms-tts-rus") |
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tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-rus") |
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def translator_mul_ru(text): |
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translation = translator_ru(translator_en(text)[0]['translation_text']) |
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return translation[0]['translation_text'] |
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def translate(audio): |
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"}) |
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return outputs["text"] |
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def synthesise(text): |
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translated_text = translator_mul_ru(text) |
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inputs = tokenizer(translated_text, return_tensors="pt") |
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input_ids = inputs["input_ids"] |
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with torch.no_grad(): |
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outputs = model(input_ids) |
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speech = outputs["waveform"] |
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return speech.cpu() |
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def speech_to_speech_translation(audio): |
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translated_text = translate(audio) |
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print(translated_text) |
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synthesised_speech = synthesise(translated_text) |
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synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) |
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return 16000, synthesised_speech[0] |
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title = "Speech-to-Speech Translation" |
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description = """ |
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* Выбранная ASR модель - https://huggingface.co/voidful/wav2vec2-xlsr-multilingual-56 |
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* Перевод текста на русский с помощью модели https://huggingface.co/Helsinki-NLP/opus-mt-mul-en |
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* Синтез речи на русском языке с помощью модели https://huggingface.co/facebook/mms-tts-rus |
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""" |
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demo = gr.Blocks() |
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mic_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(sources="microphone", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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title=title, |
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description=description, |
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) |
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file_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(sources="upload", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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title=title, |
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description=description, |
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) |
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with demo: |
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gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "File"]) |
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demo.launch() |
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