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import spaces |
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import gradio as gr |
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import librosa |
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import numpy as np |
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import torch |
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from transformers import pipeline |
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synthesiser = pipeline("text-to-speech", "techiaith/microsoft_speecht5_finetuned_bu_tts_cy_en") |
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speaker_embeddings = { |
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"GGP": "spkemb/speaker0.npy", |
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"BGP": "spkemb/speaker1.npy", |
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"BDP": "spkemb/speaker2.npy", |
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} |
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spk_model_name = "speechbrain/spkrec-xvect-voxceleb" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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print(f">>>>> DEVICE {device}") |
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speaker_model = EncoderClassifier.from_hparams( |
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source=spk_model_name, |
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run_opts={"device": device}, |
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savedir=os.path.join("/tmp", spk_model_name), |
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) |
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def create_speaker_embedding(waveform): |
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with torch.no_grad(): |
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se = speaker_model.encode_batch(torch.tensor(waveform)) |
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se = torch.nn.functional.normalize(se, dim=2) |
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se = se.squeeze().cpu().numpy() |
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return se |
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@spaces.GPU |
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def predict(text, speaker): |
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if len(text.strip()) == 0: |
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return (16000, np.zeros(0).astype(np.int16)) |
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speaker_embedding = np.load(speaker_embeddings[speaker[:3]]) |
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speaker_embedding = prepare_dataset(speaker_embedding) |
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speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0) |
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speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding}) |
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speech = (speech.numpy() * 32767).astype(np.int16) |
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return (16000, speech) |
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title = "Techiaith Finetune Microsoft/SpeechT5: Speech Synthesis" |
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description = """ |
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Lleisiau TTS microsoft_speech_T5_finetune_bu_tts_cy_en |
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""" |
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examples = [ |
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["Rhyfeddod neu ffenomenon optegol a meteorolegol yw enfys, pan fydd sbectrwm o olau yn ymddangos yn yr awyr pan fo'r haul yn disgleirio ar ddiferion o leithder yn atmosffer y ddaear.", "GGP (gwryw-gogledd-pro)"], |
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["Rhyfeddod neu ffenomenon optegol a meteorolegol yw enfys, pan fydd sbectrwm o olau yn ymddangos yn yr awyr pan fo'r haul yn disgleirio ar ddiferion o leithder yn atmosffer y ddaear.", "BGP (benyw-gogledd-pro)"], |
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["Rhyfeddod neu ffenomenon optegol a meteorolegol yw enfys, pan fydd sbectrwm o olau yn ymddangos yn yr awyr pan fo'r haul yn disgleirio ar ddiferion o leithder yn atmosffer y ddaear.", "BDP (benyw-de-pro)"], |
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] |
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gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Text(label="Input Text"), |
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gr.Radio(label="Speaker", choices=[ |
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"GGP (gwryw-gogledd-pro)", |
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"BGP (benyw-gogledd-pro)", |
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"BDP (benyw-de-pro)", |
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], |
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value="GGP (gwryw-gogledd-pro)"), |
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], |
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outputs=[ |
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gr.Audio(label="Generated Speech", type="numpy"), |
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], |
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title=title, |
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description=description, |
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examples=examples, |
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).launch() |
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