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import numpy as np |
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import soundfile as sf |
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import yaml |
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import tensorflow as tf |
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from tensorflow_tts.inference import TFAutoModel |
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from tensorflow_tts.inference import AutoProcessor |
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import gradio as gr |
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fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en") |
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mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en") |
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processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en") |
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def inference(text): |
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input_ids = processor.text_to_sequence(text) |
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mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference( |
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input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), |
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speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), |
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speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32), |
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f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), |
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energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), |
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) |
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audio_before = mb_melgan.inference(mel_before)[0, :, 0] |
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audio_after = mb_melgan.inference(mel_after)[0, :, 0] |
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sf.write('./audio_before.wav', audio_before, 22050, "PCM_16") |
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sf.write('./audio_after.wav', audio_after, 22050, "PCM_16") |
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return './audio_after.wav' |
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inputs = gr.inputs.Textbox(lines=5, label="Input Text") |
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outputs = gr.outputs.Audio(type="file", label="Output Audio") |
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title = "Tensorflow TTS" |
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description = "Gradio demo for TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." |
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article = "<p style='text-align: center'><a href='https://tensorspeech.github.io/TensorFlowTTS/'>TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2</a> | <a href='https://github.com/TensorSpeech/TensorFlowTTS'>Github Repo</a></p>" |
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examples = [ |
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["TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2."], |
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["With Tensorflow 2, we can speed-up training/inference progress, optimizer further by using fake-quantize aware and pruning, make TTS models can be run faster than real-time and be able to deploy on mobile devices or embedded systems."] |
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] |
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gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |