add models

app.py

@@ -1,3 +1,4 @@
+import json
 import os
 
 os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
@@ -22,62 +23,64 @@ def get_text(text, hps):
     return text_norm
 
 
-def tts_fn(text, speaker):
-    if len(text) > 150:
-        return "Error: Text is too long", None
-    model, hps = models[model_idx[speaker]]
-    speaker_id = speaker_idx[speaker]
-    stn_tst = get_text(text, hps)
-    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, target_speaker, 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
-    if model_idx[original_speaker] != model_idx[target_speaker]:
-        return "Error: Can not convert voice between different model", None
-
-    model, hps = models[model_idx[original_speaker]]
-    original_speaker_id = speaker_idx[original_speaker]
-    target_speaker_id = speaker_idx[target_speaker]
-
-    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)
+def create_tts_fn(model, hps, speaker_ids):
+    def tts_fn(text, speaker):
+        if len(text) > 150:
+            return "Error: Text is too long", None
+        speaker_id = speaker_ids[speaker]
+        stn_tst = get_text(text, hps)
+        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)
+
+    return tts_fn
+
+
+def create_vc_fn(model, hps, speaker_ids):
+    def vc_fn(original_speaker, target_speaker, 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
+        original_speaker_id = speaker_ids[original_speaker]
+        target_speaker_id = speaker_ids[target_speaker]
+
+        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)
+
+    return vc_fn
 
 
 if __name__ == '__main__':
     models = []
-    model_idx = []
-    speaker_idx = []
-    speakers = []
-    for i in range(0, 2):
+    with open("saved_model/names.json", "r", encoding="utf-8") as f:
+        models_names = json.load(f)
+    for i, models_name in models_names.items():
         config_path = f"saved_model/{i}/config.json"
         model_path = f"saved_model/{i}/model.pth"
+        cover_path = f"saved_model/{i}/cover.jpg"
         hps = utils.get_hparams_from_file(config_path)
         model = SynthesizerTrn(
             len(hps.symbols),
@@ -87,38 +90,50 @@ if __name__ == '__main__':
             **hps.model)
         utils.load_checkpoint(model_path, model, None)
         model.eval()
-        models.append((model, hps))
-        speakers = speakers + [f"model{i}/{x}" for x in hps.speakers]
-        model_idx = model_idx + [i] * len(hps.speakers)
-        speaker_idx = speaker_idx + list(range(0, len(hps.speakers)))
+        speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
+        speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
+
+        models.append((models_name, cover_path, speakers,
+                       create_tts_fn(model, hps, speaker_ids), create_vc_fn(model, hps, speaker_ids)))
 
     app = gr.Blocks()
 
     with app:
         gr.Markdown("# Moe Japanese TTS And Voice Conversion Using VITS Model\n\n"
                     "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.moegoe)\n\n"
-                    "unofficial demo for [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)"
+                    "unofficial demo for \n\n"
+                    "- [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)\n"
+                    "- [https://github.com/Francis-Komizu/VITS](https://github.com/Francis-Komizu/VITS)"
                     )
         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=speakers, type="index", value=speakers[0])
-                    tts_submit = gr.Button("Generate", variant="primary")
-                    tts_output1 = gr.Textbox(label="Output Message")
-                    tts_output2 = gr.Audio(label="Output Audio")
+                with gr.Tabs():
+                    for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models):
+                        with gr.TabItem(f"model{i}"):
+                            with gr.Column():
+                                gr.Markdown(f"## {models_name}\n\n"
+                                            f"![cover](file/{cover_path})")
+                                tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="こんにちは。")
+                                tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
+                                                         type="index", value=speakers[0])
+                                tts_submit = gr.Button("Generate", variant="primary")
+                                tts_output1 = gr.Textbox(label="Output Message")
+                                tts_output2 = gr.Audio(label="Output Audio")
+                                tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output1, tts_output2])
             with gr.TabItem("Voice Conversion"):
-                with gr.Column():
-                    vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index",
-                                            value=speakers[0])
-                    vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index",
-                                            value=speakers[1])
-                    vc_input3 = gr.Audio(label="Input Audio (30s limitation)")
-                    vc_submit = gr.Button("Convert", variant="primary")
-                    vc_output1 = gr.Textbox(label="Output Message")
-                    vc_output2 = gr.Audio(label="Output Audio")
-
-        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])
+                with gr.Tabs():
+                    for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models):
+                        with gr.TabItem(f"model{i}"):
+                            gr.Markdown(f"## {models_name}\n\n"
+                                        f"![cover](file/{cover_path})")
+                            vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index",
+                                                    value=speakers[0])
+                            vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index",
+                                                    value=speakers[1])
+                            vc_input3 = gr.Audio(label="Input Audio (30s limitation)")
+                            vc_submit = gr.Button("Convert", variant="primary")
+                            vc_output1 = gr.Textbox(label="Output Message")
+                            vc_output2 = gr.Audio(label="Output Audio")
+                            vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2])
 
     app.launch()

saved_model/2/config.json

@@ -0,0 +1,3 @@
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+oid sha256:cf80c1c1a185384ea9c4c8ea63964e2fc592e6d2aad3f8566b534a512ed90c28
+size 1294

saved_model/2/model.pth

@@ -0,0 +1,3 @@
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+oid sha256:16adcc6dd3f23ac4407176769f1e6843f86a5b16e04b8abb5a6a11132e6b9751
+size 476622149

saved_model/3/config.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8507d589dd869adbc8f2f49f083930db85561184afaa9dc472006434e4bb1d7e
+size 1246

saved_model/3/model.pth

@@ -0,0 +1,3 @@
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+oid sha256:60dfd6e56a1f895e3db4c054fd94d5a0362103dd5d2e19941e17dd1be41e6b11
+size 476796721

saved_model/names.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ea53575cc490320713273241bc46b30c0cb399fa1e49647a5c7e13cf5e2e444
+size 201