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import spaces |
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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 |
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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="oyemade/w2v-bert-2.0-yoruba-colab-CV16.1", device=device) |
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processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") |
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model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device) |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) |
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translation_model = pipeline("translation", "facebook/nllb-200-distilled-600M", src_lang="yor_Latn", tgt_lang="eng_Latn", device=device) |
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def translate(audio): |
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text = asr_pipe(audio)["text"] |
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translation = translation_model(text) |
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return translation[0]['translation_text'] |
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def synthesise(text): |
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inputs = processor(text=text, return_tensors="pt") |
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speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
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return speech.cpu() |
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@spaces.GPU |
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def speech_to_speech_translation(audio): |
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translated_text = translate(model, audio) |
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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 |
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iface = gr.Interface( |
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speech_to_speech_translation, |
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gr.Audio(sources="microphone", type="filepath"), |
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gr.Audio(label="Generated Speech", type="numpy"), |
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title="Neoform AI: Yoruba Speech to English Speech", |
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description="Demo for Yoruba speech translated to English Speech. NOTE: If you get an ERROR after pressing submit, give the audio some secs to load then try again.", |
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) |
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iface.launch() |