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import csv
import datetime
import os
import re
import time
import uuid
from io import StringIO
import gradio as gr
import spaces
import torch
import torchaudio
from huggingface_hub import HfApi, hf_hub_download, snapshot_download
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from vinorm import TTSnorm
# download for mecab
os.system("python -m unidic download")
HF_TOKEN = os.environ.get("HF_TOKEN")
api = HfApi(token=HF_TOKEN)
# This will trigger downloading model
print("Downloading if not downloaded viXTTS")
checkpoint_dir = "model/"
repo_id = "capleaf/viXTTS"
use_deepspeed = False
os.makedirs(checkpoint_dir, exist_ok=True)
required_files = ["model.pth", "config.json", "vocab.json", "speakers_xtts.pth"]
files_in_dir = os.listdir(checkpoint_dir)
if not all(file in files_in_dir for file in required_files):
snapshot_download(
repo_id=repo_id,
repo_type="model",
local_dir=checkpoint_dir,
)
hf_hub_download(
repo_id="coqui/XTTS-v2",
filename="speakers_xtts.pth",
local_dir=checkpoint_dir,
)
xtts_config = os.path.join(checkpoint_dir, "config.json")
config = XttsConfig()
config.load_json(xtts_config)
MODEL = Xtts.init_from_config(config)
MODEL.load_checkpoint(
config, checkpoint_dir=checkpoint_dir, use_deepspeed=use_deepspeed
)
if torch.cuda.is_available():
MODEL.cuda()
supported_languages = config.languages
if not "vi" in supported_languages:
supported_languages.append("vi")
def normalize_vietnamese_text(text):
text = (
TTSnorm(text, unknown=False, lower=False, rule=True)
.replace("..", ".")
.replace("!.", "!")
.replace("?.", "?")
.replace(" .", ".")
.replace(" ,", ",")
.replace('"', "")
.replace("'", "")
.replace("AI", "Ây Ai")
.replace("A.I", "Ây Ai")
)
return text
def calculate_keep_len(text, lang):
"""Simple hack for short sentences"""
if lang in ["ja", "zh-cn"]:
return -1
word_count = len(text.split())
num_punct = text.count(".") + text.count("!") + text.count("?") + text.count(",")
if word_count < 5:
return 15000 * word_count + 2000 * num_punct
elif word_count < 10:
return 13000 * word_count + 2000 * num_punct
return -1
@spaces.GPU
def predict(
prompt,
language,
audio_file_pth,
normalize_text=True,
):
if language not in supported_languages:
metrics_text = gr.Warning(
f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown"
)
return (None, metrics_text)
speaker_wav = audio_file_pth
if len(prompt) < 2:
metrics_text = gr.Warning("Please give a longer prompt text")
return (None, metrics_text)
try:
metrics_text = ""
t_latent = time.time()
try:
(
gpt_cond_latent,
speaker_embedding,
) = MODEL.get_conditioning_latents(
audio_path=speaker_wav,
gpt_cond_len=30,
gpt_cond_chunk_len=4,
max_ref_length=60,
)
except Exception as e:
print("Speaker encoding error", str(e))
metrics_text = gr.Warning(
"It appears something wrong with reference, did you unmute your microphone?"
)
return (None, metrics_text)
prompt = re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)", r"\1 \2\2", prompt)
if normalize_text and language == "vi":
prompt = normalize_vietnamese_text(prompt)
print("I: Generating new audio...")
t0 = time.time()
out = MODEL.inference(
prompt,
language,
gpt_cond_latent,
speaker_embedding,
repetition_penalty=5.0,
temperature=0.75,
enable_text_splitting=True,
)
inference_time = time.time() - t0
print(f"I: Time to generate audio: {round(inference_time*1000)} milliseconds")
metrics_text += (
f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
)
real_time_factor = (time.time() - t0) / out["wav"].shape[-1] * 24000
print(f"Real-time factor (RTF): {real_time_factor}")
metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"
# Temporary hack for short sentences
keep_len = calculate_keep_len(prompt, language)
out["wav"] = out["wav"][:keep_len]
torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
except RuntimeError as e:
if "device-side assert" in str(e):
# cannot do anything on cuda device side error, need tor estart
print(
f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
flush=True,
)
gr.Warning("Unhandled Exception encounter, please retry in a minute")
print("Cuda device-assert Runtime encountered need restart")
error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
error_data = [
error_time,
prompt,
language,
audio_file_pth,
]
error_data = [str(e) if type(e) != str else e for e in error_data]
print(error_data)
print(speaker_wav)
write_io = StringIO()
csv.writer(write_io).writerows([error_data])
csv_upload = write_io.getvalue().encode()
filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
print("Writing error csv")
error_api = HfApi()
error_api.upload_file(
path_or_fileobj=csv_upload,
path_in_repo=filename,
repo_id="coqui/xtts-flagged-dataset",
repo_type="dataset",
)
# speaker_wav
print("Writing error reference audio")
speaker_filename = error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
error_api = HfApi()
error_api.upload_file(
path_or_fileobj=speaker_wav,
path_in_repo=speaker_filename,
repo_id="coqui/xtts-flagged-dataset",
repo_type="dataset",
)
# HF Space specific.. This error is unrecoverable need to restart space
space = api.get_space_runtime(repo_id=repo_id)
if space.stage != "BUILDING":
api.restart_space(repo_id=repo_id)
else:
print("TRIED TO RESTART but space is building")
else:
if "Failed to decode" in str(e):
print("Speaker encoding error", str(e))
metrics_text = gr.Warning(
metrics_text="It appears something wrong with reference, did you unmute your microphone?"
)
else:
print("RuntimeError: non device-side assert error:", str(e))
metrics_text = gr.Warning(
"Something unexpected happened please retry again."
)
return (None, metrics_text)
return ("output.wav", metrics_text)
with gr.Blocks(analytics_enabled=False) as demo:
with gr.Row():
with gr.Column():
gr.Markdown(
"""
# viXTTS Demo ✨
- Github: https://github.com/thinhlpg/vixtts-demo/
- viVoice: https://github.com/thinhlpg/viVoice
"""
)
with gr.Column():
# placeholder to align the image
pass
with gr.Row():
with gr.Column():
input_text_gr = gr.Textbox(
label="Text Prompt",
info="One or two sentences at a time is better. Up to 200 text characters.",
value="Xin chào, tôi là một mô hình chuyển đổi văn bản thành giọng nói tiếng Việt",
)
language_gr = gr.Dropdown(
label="Language",
info="Select an output language for the synthesised speech",
choices=[
"vi",
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"zh-cn",
"ja",
"ko",
"hu",
"hi",
],
max_choices=1,
value="vi",
)
normalize_text = gr.Checkbox(
label="Normalize Vietnamese Text",
info="Normalize Vietnamese Text",
)
ref_gr = gr.Audio(
label="Reference Audio",
type="filepath",
value="model/samples/nu-luu-loat.wav",
)
tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
with gr.Column():
audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
out_text_gr = gr.Text(label="Metrics")
tts_button.click(
predict,
[
input_text_gr,
language_gr,
ref_gr,
normalize_text,
],
outputs=[audio_gr, out_text_gr],
api_name="predict",
)
demo.queue()
demo.launch(debug=True, show_api=True, share=True)
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