Update app.py

app.py

@@ -8,43 +8,6 @@ import scipy.io.wavfile
 from espnet2.bin.tts_inference import Text2Speech
 from espnet2.utils.types import str_or_none
 
-
-# def load_model(model_tag, vocoder_tag):
-#     from espnet_model_zoo.downloader import ModelDownloader
-
-#     kwargs = {}
-
-#     # Model
-#     d = ModelDownloader()
-#     kwargs = d.download_and_unpack(model_tag)
-
-#     # Vocoder
-#     download_dir = Path(os.path.expanduser("~/.cache/parallel_wavegan"))
-#     vocoder_dir = download_dir / vocoder_tag
-#     os.makedirs(vocoder_dir, exist_ok=True)
-
-#     kwargs["vocoder_config"] = vocoder_dir / "config.yml"
-#     if not kwargs["vocoder_config"].exists():
-#         urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/config.yml", kwargs["vocoder_config"])
-
-#     kwargs["vocoder_file"] = vocoder_dir / "checkpoint-50000steps.pkl"
-#     if not kwargs["vocoder_file"].exists():
-#         urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/checkpoint-50000steps.pkl", kwargs["vocoder_file"])
-
-#     return Text2Speech(
-#         **kwargs,
-#         device="cpu",
-#         threshold=0.5,
-#         minlenratio=0.0,
-#         maxlenratio=10.0,
-#         use_att_constraint=True,
-#         backward_window=1,
-#         forward_window=4,
-#     )
-
-# gos_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-gronings/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-gronings')
-# nld_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-dutch/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-dutch')
-
 gos_text2speech = Text2Speech.from_pretrained(
    model_tag="https://huggingface.co/wietsedv/tacotron2-gronings/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip",
    vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3",
@@ -56,42 +19,14 @@ gos_text2speech = Text2Speech.from_pretrained(
    backward_window=1,
    forward_window=4,
 )
-nld_text2speech = Text2Speech.from_pretrained(
-   model_tag="https://huggingface.co/wietsedv/tacotron2-dutch/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip",
-   vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3",
-   device="cpu",
-   threshold=0.5,
-   minlenratio=0.0,
-   maxlenratio=10.0,
-   use_att_constraint=True,
-   backward_window=1,
-   forward_window=4,
-)
-#eng_text2speech = Text2Speech.from_pretrained(
-#    model_tag="kan-bayashi/ljspeech_tacotron2",
-#    vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3",
-#    device="cpu",
-#    threshold=0.5,
-#    minlenratio=0.0,
-#    maxlenratio=10.0,
-#    use_att_constraint=True,
-#    backward_window=1,
-#    forward_window=4,
-#)
 
 def inference(text,lang):
   with torch.no_grad():
       if lang == "gronings":
           wav = gos_text2speech(text)["wav"]
           scipy.io.wavfile.write("out.wav", gos_text2speech.fs , wav.view(-1).cpu().numpy())
-      if lang == "dutch":
-          wav = nld_text2speech(text)["wav"]
-          scipy.io.wavfile.write("out.wav", nld_text2speech.fs , wav.view(-1).cpu().numpy())
-      #if lang == "english":
-      #    wav = eng_text2speech(text)["wav"]
-      #    scipy.io.wavfile.write("out.wav", eng_text2speech.fs , wav.view(-1).cpu().numpy())
 
-  return  "out.wav", "out.wav"
+  return  "out.wav"
 
 title = "GroTTS"
 examples = [
@@ -100,7 +35,7 @@ examples = [
 
 gr.Interface(
     inference,
-    [gr.inputs.Textbox(label="input text", lines=3), gr.inputs.Radio(choices=["gronings", "dutch"], type="value", default="gronings", label="language")], 
+    [gr.inputs.Textbox(label="input text", lines=3), gr.inputs.Radio(choices=["gronings"], type="value", default="gronings", label="language")], 
     [gr.outputs.Audio(type="file", label="Output"), gr.outputs.File()],
     title=title,
     examples=examples