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Update app.py
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app.py
CHANGED
@@ -9,7 +9,7 @@ from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Proce
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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# load speech translation checkpoint
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-
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# load text-to-speech checkpoint and speaker embeddings
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processor = SpeechT5Processor.from_pretrained("ckandemir/speecht5_finetuned_voxpopuli_fr")
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@@ -21,12 +21,41 @@ embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validat
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speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "
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return outputs["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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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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# load speech translation checkpoint
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
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# load text-to-speech checkpoint and speaker embeddings
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processor = SpeechT5Processor.from_pretrained("ckandemir/speecht5_finetuned_voxpopuli_fr")
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speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
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replacements = [
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("á", "a"),
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("ç", "c"),
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("è", "e"),
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("ì", "i"),
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("í", "i"),
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("ò", "o"),
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("ó", "o"),
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("ù", "u"),
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("ú", "u"),
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("š", "s"),
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("ï", "i"),
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("ñ", "n"),
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("ü", "u"),
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]
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def cleanup_text(text):
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for src, dst in replacements:
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text = text.replace(src, dst)
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return text
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def synthesize_speech(text):
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text = cleanup_text(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 gr.Audio.update(value=(16000, speech.cpu().numpy()))
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "french"})
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return outputs["text"]
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def synthesise(text):
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text = cleanup_text(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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