Create app.py

app.py

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+import numpy as np
+import soundfile as sf
+import yaml
+
+import tensorflow as tf
+
+from tensorflow_tts.inference import TFAutoModel
+from tensorflow_tts.inference import AutoProcessor
+import gradio as gr
+
+# initialize fastspeech2 model.
+fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")
+
+
+# initialize mb_melgan model
+mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en")
+
+
+# inference
+processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en")
+
+def inference(text):
+  input_ids = processor.text_to_sequence(text)
+  # fastspeech inference
+  
+  mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
+      input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
+      speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
+      speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
+      f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
+      energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
+  )
+  
+  # melgan inference
+  audio_before = mb_melgan.inference(mel_before)[0, :, 0]
+  audio_after = mb_melgan.inference(mel_after)[0, :, 0]
+  
+  # save to file
+  sf.write('./audio_before.wav', audio_before, 22050, "PCM_16")
+  sf.write('./audio_after.wav', audio_after, 22050, "PCM_16")
+  return './audio_after.wav'
+  
+inputs = gr.inputs.Textbox(lines=5, label="Input Text")
+outputs =  gr.outputs.Audio(type="file", label="Output Audio")
+
+
+title = "Tensorflow TTS"
+description = "demo for VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2106.06103'>Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech</a> | <a href='https://github.com/jaywalnut310/vits'>Github Repo</a></p>"
+
+examples = [
+ ["We propose VITS, Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech."],
+ ["Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling."]   
+]
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()