initial version

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+import time
+import urllib.request
+from pathlib import Path
+import os
+import torch
+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')
+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"
+
+title = "GroTTS"
+examples = [
+  ['Ze gingen mit klas noar Waddendiek. Over en deur bragel lopen.', 'gronings']
+]
+
+gr.Interface(
+    inference,
+    [gr.inputs.Textbox(label="input text", lines=3), gr.inputs.Radio(choices=["gronings", "dutch", "english"], type="value", default="gronings", label="language")], 
+    [gr.outputs.Audio(type="file", label="Output"), gr.outputs.File()],
+    title=title,
+    examples=examples
+    ).launch(enable_queue=True, debug=True)

packages.txt

@@ -0,0 +1,2 @@
+cmake
+libsndfile1

requirements.txt

@@ -0,0 +1,5 @@
+espnet==0.10.3 
+parallel_wavegan==0.5.3 
+espnet_model_zoo
+scipy
+torch