Hugging Face's logo

Spaces:
Laronix
/
Laronix_ASR_TTS_VC
Sleeping

App Files Community
Laronix_ASR_TTS_VC
File size: 4,904 Bytes 
f5460b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
 
"""
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
# 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")
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' 
# tag = "kan-bayashi/vctk_multi_spk_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())

def ASRTTS(audio_file, spk_name, ref_text=""):
    spk = spks[spk_name]
    spembs = xvectors[spk]
    if ref_text == "":
        reg_text = transcriber(audio_file)['text']
    else:
        reg_text = ref_text
    
    speech, sr = torchaudio.load(audio_file, channels_first=True) # Mono channel
    wav_tensor_spembs = text2speech(text=reg_text, speech=speech, spembs=spembs)["wav"]
    wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu")
    sample_rate = 22050
    save_id = "./wav/" + Path(audio_file).stem + "_" + spk_name +"_spkembs.wav"
    torchaudio.save(save_id, src=wav_tensor_spembs.unsqueeze(0).to("cpu"), sample_rate=22050)

    return save_id, reg_text

def ref_reg_callback(audio_file, spk_name, ref_text):
    reg_text = ref_text
    return audio_file, spk_name, reg_text
    
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="microphone",
            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', ""]]

# ASRTTS(*examples[0])
iface = gr.Interface(
    fn = ASRTTS, 
    inputs = [
        input_audio,
        speaker_option,
        reference_textbox,
    ],
    outputs = [
        output_audio,
        recognization_textbox
    ],
    examples = examples
)
iface.input_callback = ref_reg_callback
iface.launch(share=False)