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""" | |
TODO: | |
+ [x] Load Configuration | |
+ [ ] Checking | |
+ [ ] Better saving directory | |
""" | |
import numpy as np | |
from pathlib import Path | |
import jiwer | |
import pdb | |
import torch.nn as nn | |
import torch | |
import torchaudio | |
from transformers import pipeline | |
# from time import process_time, time | |
from pathlib import Path | |
import time | |
# local import | |
import sys | |
from espnet2.bin.tts_inference import Text2Speech | |
# pdb.set_trace() | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
sys.path.append("src") | |
import gradio as gr | |
# ASR part | |
audio_files = [ | |
str(x) | |
for x in sorted( | |
Path( | |
"/home/kevingeng/Disk2/laronix/laronix_automos/data/20230103_video" | |
).glob("**/*wav") | |
) | |
] | |
# audio_files = [str(x) for x in sorted(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", | |
) | |
old_transcriber = pipeline( | |
"automatic-speech-recognition", "facebook/wav2vec2-base-960h" | |
) | |
# transcriber = pipeline("automatic-speech-recognition", model="KevinGeng/PAL_John_128_p326_300_train_dev_test_seed_1") | |
# 【Female】kan-bayashi ljspeech parallel wavegan | |
# tts_model = Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits") | |
# 【Male】fastspeech2-en-200_speaker-cv4, hifigan vocoder | |
# pdb.set_trace() | |
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub | |
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface | |
# @title English multi-speaker pretrained model { run: "auto" } | |
lang = "English" | |
tag = "kan-bayashi/libritts_xvector_vits" | |
# vits needs no | |
vocoder_tag = "parallel_wavegan/vctk_parallel_wavegan.v1.long" # @param ["none", "parallel_wavegan/vctk_parallel_wavegan.v1.long", "parallel_wavegan/vctk_multi_band_melgan.v2", "parallel_wavegan/vctk_style_melgan.v1", "parallel_wavegan/vctk_hifigan.v1", "parallel_wavegan/libritts_parallel_wavegan.v1.long", "parallel_wavegan/libritts_multi_band_melgan.v2", "parallel_wavegan/libritts_hifigan.v1", "parallel_wavegan/libritts_style_melgan.v1"] {type:"string"} | |
from espnet2.bin.tts_inference import Text2Speech | |
from espnet2.utils.types import str_or_none | |
text2speech = Text2Speech.from_pretrained( | |
model_tag=str_or_none(tag), | |
vocoder_tag=str_or_none(vocoder_tag), | |
device="cuda", | |
use_att_constraint=False, | |
backward_window=1, | |
forward_window=3, | |
speed_control_alpha=1.0, | |
) | |
import glob | |
import os | |
import numpy as np | |
import kaldiio | |
# Get model directory path | |
from espnet_model_zoo.downloader import ModelDownloader | |
d = ModelDownloader() | |
model_dir = os.path.dirname(d.download_and_unpack(tag)["train_config"]) | |
# Speaker x-vector selection | |
xvector_ark = [ | |
p | |
for p in glob.glob( | |
f"{model_dir}/../../dump/**/spk_xvector.ark", recursive=True | |
) | |
if "tr" in p | |
][0] | |
xvectors = {k: v for k, v in kaldiio.load_ark(xvector_ark)} | |
spks = list(xvectors.keys()) | |
male_spks = { | |
"M1": "2300_131720", | |
"M2": "1320_122612", | |
"M3": "1188_133604", | |
"M4": "61_70970", | |
} | |
female_spks = {"F1": "2961_961", "F2": "8463_287645", "F3": "121_121726"} | |
spks = dict(male_spks, **female_spks) | |
spk_names = sorted(spks.keys()) | |
## 20230224 Mousa: No reference, | |
def ASRold(audio_file): | |
reg_text = old_transcriber(audio_file)["text"] | |
return reg_text | |
def ASRnew(audio_file, state=""): | |
# pdb.set_trace() | |
time.sleep(2) | |
reg_text = transcriber(audio_file)["text"] | |
state += reg_text + "\n" | |
return state, state | |
def VAD(audio_file): | |
# pdb.set_trace() | |
reg_text = transcriber(audio_file)["text"] | |
return 1 | |
reference_textbox = gr.Textbox( | |
value="", | |
placeholder="Input reference here", | |
label="Reference", | |
) | |
recognization_textbox = gr.Textbox( | |
value="", | |
placeholder="Output recognization here", | |
label="recognization_textbox", | |
) | |
speaker_option = gr.Radio(choices=spk_names, label="Speaker") | |
input_audio = gr.Audio( | |
source="upload", type="filepath", label="Audio_to_Evaluate" | |
) | |
output_audio = gr.Audio( | |
source="upload", file="filepath", label="Synthesized Audio" | |
) | |
examples = [ | |
["./samples/001.wav", "M1", ""], | |
["./samples/002.wav", "M2", ""], | |
["./samples/003.wav", "F1", ""], | |
["./samples/004.wav", "F2", ""], | |
] | |
def change_audiobox(choice): | |
if choice == "upload": | |
input_audio = gr.Audio.update(source="upload", visible=True) | |
elif choice == "microphone": | |
input_audio = gr.Audio.update(source="microphone", visible=True) | |
else: | |
input_audio = gr.Audio.update(visible=False) | |
return input_audio | |
demo = gr.Interface( | |
fn=ASRnew, | |
inputs=[ | |
gr.Audio(source="microphone", type="filepath", streaming=True), | |
"state" | |
], | |
outputs=[ | |
"textbox", | |
"state" | |
], | |
live=True) | |
# ASRnew(["/home/kevingeng/Disk2/laronix/Laronix_ASR_TTS_VC/wav/20221228_video_good_normed_5/take1_001_norm.wav", "state"]) | |
# VAD("/home/kevingeng/Disk2/laronix/Laronix_ASR_TTS_VC/wav/20221228_video_good_normed_5/take1_001_norm.wav") | |
demo.launch(share=False) |