update specch example
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@ -200,7 +200,6 @@ torch_device = "cuda"
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bddm = DiffusionPipeline.from_pretrained("fusing/diffwave-vocoder")
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# load tacotron2 to get the mel spectograms
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tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')
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tacotron2 = tacotron2.to(torch_device).eval()
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@ -209,12 +208,15 @@ text = "Hello world, I missed you so much."
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utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tts_utils')
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sequences, lengths = utils.prepare_input_sequence([text])
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# generate mel spectograms using text
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with torch.no_grad():
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mel, _, _ = tacotron2.infer(sequences, lengths)
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mel_spec, _, _ = tacotron2.infer(sequences, lengths)
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# generate the speech by passing mel spectograms to BDDM pipeline
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generator = torch.manual_seed(0)
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audio = bddm(mel, generator, torch_device)
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audio = bddm(mel_spec, generator, torch_device)
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# save generated audio
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from scipy.io.wavfile import write as wavwrite
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sampling_rate = 22050
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wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
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