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import torch |
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import torchaudio |
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import gradio as gr |
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device="cpu" |
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bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH |
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processor = bundle.get_text_processor() |
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tacotron2 = bundle.get_tacotron2().to(device) |
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waveglow = torch.hub.load( |
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"NVIDIA/DeepLearningExamples:torchhub", |
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"nvidia_waveglow", |
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model_math="fp32", |
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pretrained=False, |
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) |
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checkpoint = torch.hub.load_state_dict_from_url( |
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"https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth", |
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progress=False, |
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map_location=device, |
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) |
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state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()} |
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waveglow.load_state_dict(state_dict) |
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waveglow = waveglow.remove_weightnorm(waveglow) |
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waveglow = waveglow.to(device) |
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waveglow.eval() |
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def inference(text): |
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with torch.inference_mode(): |
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processed, lengths = processor(text) |
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processed = processed.to(device) |
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lengths = lengths.to(device) |
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spec, _, _ = tacotron2.infer(processed, lengths) |
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with torch.no_grad(): |
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waveforms = waveglow.infer(spec) |
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torchaudio.save("_assets/output_waveglow.wav", waveforms[0:1].cpu(), sample_rate=22050) |
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return "output_waveglow.wav" |
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gr.Interface(inference,"text",gr.outputs.Audio(type="file")).launch(debug=True) |
