Create app.py

app.py

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+import gradio as gr
+import os
+os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
+
+import json
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+
+import commons
+import utils
+from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
+from models import SynthesizerTrn
+from text.symbols import symbols as symbols_default 
+
+from scipy.io.wavfile import write
+from text import cleaners
+
+model_configs = {
+    "Graphemes": {
+        "path": "french_model_vits/G_700000.pth",
+        "symbols": symbols_default
+    }
+}
+
+# Global variables 
+net_g = None
+symbols = []
+_symbol_to_id = {}
+_id_to_symbol = {}
+
+def text_to_sequence(text, cleaner_names):
+    sequence = []
+    clean_text = _clean_text(text, cleaner_names)
+    for symbol in clean_text:
+        symbol_id = _symbol_to_id[symbol]
+        sequence += [symbol_id]
+    return sequence
+
+def _clean_text(text, cleaner_names):
+    for name in cleaner_names:
+        cleaner = getattr(cleaners, name)
+        if not cleaner:
+            raise Exception('Unknown cleaner: %s' % name)
+        text = cleaner(text)
+    return text
+
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if (hps.data.add_blank):
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+def load_model_and_symbols(tab_name):
+    global net_g, symbols, _symbol_to_id, _id_to_symbol
+    model_config = model_configs[tab_name]
+    symbols = model_config["symbols"]
+    _symbol_to_id = {s: i for i, s in enumerate(symbols)}
+    _id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model)
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model_config["path"], net_g, None)
+
+def tts(text, speaker_id, tab_name):
+    load_model_and_symbols(tab_name)
+    sid = torch.LongTensor([speaker_id])  # speaker identity
+    stn_tst = get_text(text, hps)
+
+    with torch.no_grad():
+        x_tst = stn_tst.unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
+            0, 0].data.float().numpy()
+    return "Success", (hps.data.sampling_rate, audio)
+
+def create_tab(tab_name):
+    with gr.TabItem(tab_name):
+        gr.Markdown(f"### {tab_name} TTS Model")
+        tts_input1 = gr.TextArea(label="Text in french", value="")
+        tts_input2 = gr.Dropdown(label="Speaker", choices=["Male", "Female"], type="index", value="Male")
+        tts_submit = gr.Button("Generate", variant="primary")
+        tts_output1 = gr.Textbox(label="Message")
+        tts_output2 = gr.Audio(label="Output")
+        tts_submit.click(lambda text, speaker_id: tts(text, speaker_id, tab_name), [tts_input1, tts_input2], [tts_output1, tts_output2])
+
+hps = utils.get_hparams_from_file("configs/vctk_base.json")
+
+app = gr.Blocks()
+with app:
+    gr.Markdown(
+        """
+        # VITS Implementation for French
+        
+        Based on VITS (https://github.com/jaywalnut310/vits).
+        
+        ## How to use:
+        Write the text on the box below. For faster inference, it is recommended to use short sentences.
+        
+        ## Hint: Some sample texts are available at the bottom of the web site.
+        """
+    )
+    with gr.Tabs():
+        create_tab("French TTS")
+
+    gr.Markdown(
+        """
+        ## Examples
+        | Input Text | Speaker |
+        |------------|---------|
+        | On ne voit bien qu'avec le cœur, l'essentiel est invisible pour les yeux. | Female | 
+        | Voilà plusieurs fois, Monsieur, que je vous rencontre sur mon chemin. C’est autant de fois de trop, et j’en ai assez de perdre mon temps à déjouer les pièges que vous me tendez. | Male |
+        | Je pense, donc je suis.  | Female | 
+        | La vie est un sommeil, l'amour en est le rêve, et vous aurez vécu si vous avez aimé. | Male | 
+        """
+    )
+
+app.launch()