Upload streamlit.py

streamlit.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow_tts.inference import TFAutoModel
+from tensorflow_tts.inference import AutoConfig
+from tensorflow_tts.inference import AutoProcessor
+
+st.title("Text-to-Speech Synthesis")
+
+# Sidebar
+model_selection = st.sidebar.selectbox("Select Model", [
+    "Tacotron2 + MelGAN",
+    "Tacotron2 + MelGAN-STFT",
+    "Tacotron2 + MB-MelGAN",
+    "FastSpeech + MB-MelGAN",
+    "FastSpeech + MelGAN-STFT",
+    "FastSpeech + MelGAN",
+    "FastSpeech2 + MB-MelGAN",
+    "FastSpeech2 + MelGAN-STFT",
+    "FastSpeech2 + MelGAN"
+])
+
+input_text = st.text_area("Enter Text", value="Bill got in the habit of asking himself “Is that thought true?” And if he wasn’t absolutely certain it was, he just let it go.")
+
+# Load models and configurations
+tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en", name="tacotron2")
+fastspeech = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech-ljspeech-en", name="fastspeech")
+fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en", name="fastspeech2")
+melgan = TFAutoModel.from_pretrained("tensorspeech/tts-melgan-ljspeech-en", name="melgan")
+melgan_stft_config = AutoConfig.from_pretrained('TensorFlowTTS/examples/melgan_stft/conf/melgan_stft.v1.yaml')
+melgan_stft = TFAutoModel.from_pretrained(
+    config=melgan_stft_config,
+    pretrained_path="melgan.stft-2M.h5",
+    name="melgan_stft"
+)
+mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en", name="mb_melgan")
+processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
+
+def do_synthesis(input_text, text2mel_model, vocoder_model, text2mel_name, vocoder_name):
+    input_ids = processor.text_to_sequence(input_text)
+
+    if text2mel_name == "TACOTRON":
+        _, mel_outputs, _, _ = text2mel_model.inference(
+            tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
+            tf.convert_to_tensor([len(input_ids)], tf.int32),
+            tf.convert_to_tensor([0], dtype=tf.int32)
+        )
+    elif text2mel_name == "FASTSPEECH":
+        _, mel_outputs, _ = text2mel_model.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),
+        )
+    elif text2mel_name == "FASTSPEECH2":
+        _, mel_outputs, _, _, _ = text2mel_model.inference(
+            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),
+        )
+    else:
+        raise ValueError("Only TACOTRON, FASTSPEECH, FASTSPEECH2 are supported on text2mel_name")
+
+    if vocoder_name == "MELGAN" or vocoder_name == "MELGAN-STFT":
+        audio = vocoder_model(mel_outputs)[0, :, 0]
+    elif vocoder_name == "MB-MELGAN":
+        audio = vocoder_model(mel_outputs)[0, :, 0]
+    else:
+        raise ValueError("Only MELGAN, MELGAN-STFT and MB_MELGAN are supported on vocoder_name")
+
+    return mel_outputs.numpy(), audio.numpy()
+
+if st.button("Synthesize"):
+    # Perform synthesis based on selected model
+    if model_selection == "Tacotron2 + MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, tacotron2, melgan, "TACOTRON", "MELGAN")
+    elif model_selection == "Tacotron2 + MelGAN-STFT":
+        mel_outputs, audio = do_synthesis(input_text, tacotron2, melgan_stft, "TACOTRON", "MELGAN-STFT")
+    elif model_selection == "Tacotron2 + MB-MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, tacotron2, mb_melgan, "TACOTRON", "MB-MELGAN")
+    elif model_selection == "FastSpeech + MB-MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech, mb_melgan, "FASTSPEECH", "MB-MELGAN")
+    elif model_selection == "FastSpeech + MelGAN-STFT":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech, melgan_stft, "FASTSPEECH", "MELGAN-STFT")
+    elif model_selection == "FastSpeech + MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech, melgan, "FASTSPEECH", "MELGAN")
+    elif model_selection == "FastSpeech2 + MB-MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech2, mb_melgan, "FASTSPEECH2", "MB-MELGAN")
+    elif model_selection == "FastSpeech2 + MelGAN-STFT":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech2, melgan_stft, "FASTSPEECH2", "MELGAN-STFT")
+    elif model_selection == "FastSpeech2 + MelGAN":
+        mel_outputs, audio = do_synthesis(input_text, fastspeech2, melgan, "FASTSPEECH2", "MELGAN")
+
+    # Visualize mel spectrogram
+    mels = np.reshape(mel_outputs, [-1, 80])
+    fig = plt.figure(figsize=(10, 8))
+    ax1 = fig.add_subplot(311)
+    ax1.set_title(f'Predicted Mel-after-Spectrogram')
+    im = ax1.imshow(np.rot90(mels), aspect='auto', interpolation='none')
+    fig.colorbar(mappable=im, shrink=0.65, orientation='horizontal', ax=ax1)
+    st.pyplot(fig)
+
+    # Display the audio using the specified sample rate
+    st.audio(audio, format="audio/wav", sample_rate=22050, start_time=0)
+
+if __name__ == '__main__':
+    app()