import torch import torchaudio import gradio as gr device="cpu" bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH processor = bundle.get_text_processor() tacotron2 = bundle.get_tacotron2().to(device) # Workaround to load model mapped on GPU # https://stackoverflow.com/a/61840832 waveglow = torch.hub.load( "NVIDIA/DeepLearningExamples:torchhub", "nvidia_waveglow", model_math="fp32", pretrained=False, ) checkpoint = torch.hub.load_state_dict_from_url( "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth", # noqa: E501 progress=False, map_location=device, ) state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()} waveglow.load_state_dict(state_dict) waveglow = waveglow.remove_weightnorm(waveglow) waveglow = waveglow.to(device) waveglow.eval() def inference(text): with torch.inference_mode(): processed, lengths = processor(text) processed = processed.to(device) lengths = lengths.to(device) spec, _, _ = tacotron2.infer(processed, lengths) with torch.no_grad(): waveforms = waveglow.infer(spec) torchaudio.save("output_waveglow.wav", waveforms[0:1].cpu(), sample_rate=22050) return "output_waveglow.wav",spec[0].cpu().detach().numpu() title="TACOTRON 2" description="Gradio demo for TACOTRON 2: The Tacotron 2 model for generating mel spectrograms from text. To use it, simply add you text or click on one of the examples to load them. Read more at the links below." article = "
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions | Github Repo
" examples=[["life is like a box of chocolates"]] gr.Interface(inference,"text",[gr.outputs.Audio(type="file"),gr.outputs.Image(type="numpy",label="Spectrogram")],title=title,description=description,article=article,examples=examples).launch(enable_queue=True)