Merge branch 'main' into VCAM-27

app.py

@@ -91,12 +91,13 @@ xvectors = {k: v for k, v in kaldiio.load_ark(xvector_ark)}
 spks = list(xvectors.keys())
 
 male_spks = {
-    "Male1": "2300_131720",
+    "Male1": "260_123286",
     "Male2": "1320_122612",
+    "Male3": "672_122797"
 }
     # "M3": "1188_133604",
     # "M4": "61_70970",
-female_spks = {"Female1": "2961_961", "Female2": "8463_287645", }
+female_spks = {"Female1": "5683_32865", "Female2": "121_131726", "Female3": "8463_287645"}
 # "F3": "121_121726"
 spks = dict(male_spks, **female_spks)
 spk_names = sorted(spks.keys())

app.ver1.py

@@ -1,72 +0,0 @@
-#TODO:
-#  + [x] Load Configuration
-#  + [ ] Checking
-#  + [ ] Better saving directory
-
-from pathlib import Path
-from transformers import pipeline
-import torch.nn as nn
-import torch
-import torchaudio
-import gradio as gr
-import sys
-
-# Local imports
-sys.path.append("src")
-from espnet2.bin.tts_inference import Text2Speech
-from espnet2.utils.types import str_or_none
-
-# Check if GPU is available
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# ASR part
-
-data_path = "/home/kevingeng/Disk2/laronix/laronix_automos/data/20230103_video"
-audio_files = sorted(list(Path(data_path).glob("**/*wav")))
-# audio_files = sorted(list(Path("./data/Patient_sil_trim_16k_normed_5_snr_40/Rainbow").glob("**/*wav")))
-
-transcriber = pipeline("automatic-speech-recognition", model="KevinGeng/PAL_John_128_train_dev_test_seed_1")
-
-# TTS part
-def load_model(lang, tag, vocoder_tag):
-    if lang == "Japanese":
-        if tag == "kan-bayashi/ljspeech_parallel_wavegan":
-            tts_model = Text2Speech.from_pretrained("kan-bayashi/ljspeech_parallel_wavegan")
-        elif tag == "kan-bayashi/ljspeech_merlin_multi_band_melgan":
-            tts_model = Text2Speech.from_pretrained("kan-bayashi/ljspeech_merlin_multi_band_melgan")
-        else:
-            raise ValueError(f"Not supported: lang={lang}, tag={tag}")
-        vocoder = None if vocoder_tag == "none" else vocoder_tag
-    elif lang == "English":
-        # VITS needs no vocoder; others do
-        if tag == "kan-bayashi/libritts_xvector_vits":
-            tts_model = Text2Speech.from_pretrained("kan-bayashi/libritts_xvector_vits")
-            vocoder = None
-        elif tag == "kan-bayashi/fastspeech2_en_libritts_guessspeaker_melgan.v3":
-            tts_model = Text2Speech.from_pretrained("kan-bayashi/fastspeech2_en_libritts_guessspeaker_melgan.v3")
-            vocoder = "melgan"
-        else:
-            raise ValueError(f"Not supported: lang={lang}, tag={tag}")
-    else:
-        raise ValueError(f"Not supported: lang={lang}")
-    return tts_model, vocoder
-
-tts_model, vocoder_tag = load_model(lang="English", tag="kan-bayashi/libritts_xvector_vits", vocoder_tag="parallel_wavegan/vctk_parallel_wavegan.v1.long")
-tts_model = tts_model.to(device)
-
-vocoder = None if vocoder_tag == "none" else torchaudio.models.vocoder.from_pretrained(vocoder_tag).to(device)
-
-# Gradio part
-def synthesize(text):
-    with torch.no_grad():
-        # Text-to-speech
-        wav = tts_model(text)[0]
-        if vocoder is not None:
-            # Apply vocoder
-            wav = vocoder.inference(wav)
-        # Convert to numpy array
-        wav = wav.squeeze().cpu().numpy()
-    return wav
-
-interface = gr.Interface(synthesize, inputs="text", outputs="audio")
-interface.launch()