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import re, io, os, stat, logging
import tempfile, subprocess
import requests
import torch
import traceback
import numpy as np
import scipy
from TTS.api import TTS
from flask import Flask, Blueprint, request, jsonify, send_file
import torch
import torchaudio
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
model_dir=os.environ.get("MODEL_DIR")
model_path=model_dir
config_path=f'{model_dir}/config.json'
vocoder_config_path=f'{model_dir}/vocab.json'
app = Flask(__name__)
# def upload_bytes(bytes, ext=".wav"):
# return bytes
from qili import upload_bytes
# if __name__ == "__main__":
# app = Flask(__name__)
# else:
# app = Blueprint("xtts", __name__)
tts=None
model=None
sample_root= os.environ.get('XTTS_SAMPLE_DIR')
if(sample_root==None):
sample_root=f'{os.getcwd()}/samples'
if not os.path.exists(sample_root):
os.makedirs(sample_root)
default_sample=f'{os.path.dirname(os.path.abspath(__file__))}/sample.wav', f'{sample_root}/sample.pt'
ffmpeg=f'{os.path.dirname(os.path.abspath(__file__))}/ffmpeg'
if tts is None:
model_name="tts_models/multilingual/multi-dataset/xtts_v2"
logging.info(f"loading model {model_name} ...")
tts = TTS(
model_path=model_path,
config_path=config_path,
vocoder_config_path=vocoder_config_path,
progress_bar=False
)
model=tts.synthesizer.tts_model
#hack to use cache
model.__get_conditioning_latents=model.get_conditioning_latents
model.get_conditioning_latents=get_conditioning_latents
logging.info("model is ready")
@app.route("/convert")
def predict():
global tts
global model
text = request.args.get('text')
sample = request.args.get('sample')
language = request.args.get('language')
if text is None:
return jsonify({'error': 'text is missing'}), 400
text= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",text)
try:
wav = tts.tts(
text,
language=language if language is not None else "zh",
speaker_wav=sample if sample is not None else default_sample[0],
)
with io.BytesIO() as wav_buffer:
if torch.is_tensor(wav):
wav = wav.cpu().numpy()
if isinstance(wav, list):
wav = np.array(wav)
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
wav_norm = wav_norm.astype(np.int16)
scipy.io.wavfile.write(wav_buffer, tts.synthesizer.output_sample_rate, wav_norm)
wav_bytes = wav_buffer.getvalue()
url= upload_bytes(wav_bytes, ext=".wav")
logging.debug(f'wav is at {url}')
return url
except Exception as e:
traceback.print_exc()
return str(e)
@app.route("/")
def play():
url=predict()
return f'''
<html>
<body>
<audio controls autoplay>
<source src="{url}" type="audio/wav">
Your browser does not support the audio element.
</audio>
</body>
</html>
'''
def get_conditioning_latents(audio_path, **others):
global model
speaker_wav, pt_file=download(audio_path)
try:
if pt_file != None:
(
gpt_cond_latent,
speaker_embedding,
) = torch.load(pt_file)
logging.debug(f'sample wav info loaded from {pt_file}')
except:
(
gpt_cond_latent,
speaker_embedding,
) = model.__get_conditioning_latents(audio_path=speaker_wav, **others)
torch.save((gpt_cond_latent,speaker_embedding), pt_file)
logging.debug(f'sample wav info saved to {pt_file}')
return gpt_cond_latent,speaker_embedding
def download(url):
try:
response = requests.get(url)
if response.status_code == 200:
id=f'{sample_root}/{response.headers["etag"]}.pt'.replace('"','')
if(os.path.exists(id)):
return "", id
with tempfile.NamedTemporaryFile(mode="wb", delete=True) as temp_file:
temp_file.write(response.content)
return trim_sample_audio(os.path.abspath(temp_file.name)), id
except:
return default_sample
def trim_sample_audio(speaker_wav):
global ffmpeg
try:
lowpass_highpass = "lowpass=8000,highpass=75,"
trim_silence = "areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,"
out_filename=speaker_wav.replace(".wav","_trimed.wav")
shell_command = f"{ffmpeg} -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(" ")
subprocess.run(
[item for item in shell_command],
capture_output=False,
text=True,
check=True,
)
return out_filename
except:
traceback.print_exc()
return speaker_wav
logging.info("xtts is ready") |