Update app.py

app.py

@@ -1,7 +1,92 @@
+import os
+
+os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
+
+import librosa
+import numpy as np
+import torch
+from torch import no_grad, LongTensor
+import commons
+import utils
 import gradio as gr
+from models import SynthesizerTrn
+from text import text_to_sequence
+from mel_processing import spectrogram_torch
+
+
+def get_text(text):
+    text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm
+
+
+def tts_fn(text, speaker_id):
+    if len(text) > 150:
+        return "Error: Text is too long", None
+    stn_tst = get_text(text)
+    with no_grad():
+        x_tst = stn_tst.unsqueeze(0)
+        x_tst_lengths = LongTensor([stn_tst.size(0)])
+        sid = LongTensor([speaker_id])
+        audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
+            0, 0].data.cpu().float().numpy()
+    return "Success", (hps.data.sampling_rate, audio)
+
+
+def vc_fn(original_speaker_id, target_speaker_id, input_audio):
+    if input_audio is None:
+        return "You need to upload an audio", None
+    sampling_rate, audio = input_audio
+    duration = audio.shape[0] / sampling_rate
+    if duration > 30:
+        return "Error: Audio is too long", None
+    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+    if len(audio.shape) > 1:
+        audio = librosa.to_mono(audio.transpose(1, 0))
+    if sampling_rate != hps.data.sampling_rate:
+        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
+    y = torch.FloatTensor(audio)
+    y = y.unsqueeze(0)
+    spec = spectrogram_torch(y, hps.data.filter_length,
+                             hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
+                             center=False)
+    spec_lengths = LongTensor([spec.size(-1)])
+    sid_src = LongTensor([original_speaker_id])
+    sid_tgt = LongTensor([target_speaker_id])
+    with no_grad():
+        audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
+            0, 0].data.cpu().float().numpy()
+    return "Success", (hps.data.sampling_rate, audio)
+
+
+if __name__ == '__main__':
+    config_path = "saved_model/config.json"
+    model_path = "saved_model/model.pth"
+    hps = utils.get_hparams_from_file(config_path)
+    model = SynthesizerTrn(
+        len(hps.symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model)
+    utils.load_checkpoint(model_path, model, None)
+    model.eval()
+
+    app = gr.Blocks()
+
+    with app:
+        with gr.Tabs():
+            with gr.TabItem("TTS"):
+                with gr.Column():
+                    tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="こんにちは。")
+                    tts_input2 = gr.Dropdown(label="Speaker", choices=hps.speakers, type="index", value=hps.speakers[0])
+                    tts_submit = gr.Button("Generate", variant="primary")
+                    tts_output1 = gr.Textbox(label="Output Message")
+                    tts_output2 = gr.Audio(label="Output Audio")
 
-def greet(name):
-    return "Hello " + name + "!!"
+        tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output1, tts_output2])
+        vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2])
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    app.launch()