mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
from web.api import api_blueprint
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from pathlib import Path
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from gevent import pywsgi as wsgi
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from flask import Flask, Response, request, render_template
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from synthesizer.inference import Synthesizer
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from encoder import inference as encoder
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from vocoder.hifigan import inference as gan_vocoder
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from vocoder.wavernn import inference as rnn_vocoder
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import numpy as np
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import re
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from scipy.io.wavfile import write
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import io
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import base64
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from flask_cors import CORS
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from flask_wtf import CSRFProtect
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def webApp():
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# Init and load config
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app = Flask(__name__, instance_relative_config=True)
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app.config.from_object("web.config.default")
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app.config['RESTPLUS_MASK_SWAGGER'] = False
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app.register_blueprint(api_blueprint)
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# CORS(app) #允许跨域，注释掉此行则禁止跨域请求
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csrf = CSRFProtect(app)
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csrf.init_app(app)
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syn_models_dirt = "synthesizer/saved_models"
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synthesizers = list(Path(syn_models_dirt).glob("**/*.pt"))
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synthesizers_cache = {}
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encoder.load_model(Path("encoder/saved_models/pretrained.pt"))
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gan_vocoder.load_model(Path("vocoder/saved_models/pretrained/g_hifigan.pt"))
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def pcm2float(sig, dtype='float32'):
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"""Convert PCM signal to floating point with a range from -1 to 1.
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Use dtype='float32' for single precision.
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Parameters
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----------
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sig : array_like
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Input array, must have integral type.
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dtype : data type, optional
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Desired (floating point) data type.
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Returns
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-------
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numpy.ndarray
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Normalized floating point data.
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See Also
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--------
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float2pcm, dtype
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"""
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sig = np.asarray(sig)
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if sig.dtype.kind not in 'iu':
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raise TypeError("'sig' must be an array of integers")
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dtype = np.dtype(dtype)
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if dtype.kind != 'f':
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raise TypeError("'dtype' must be a floating point type")
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i = np.iinfo(sig.dtype)
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abs_max = 2 ** (i.bits - 1)
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offset = i.min + abs_max
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return (sig.astype(dtype) - offset) / abs_max
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# Cache for synthesizer
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@csrf.exempt
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@app.route("/api/synthesize", methods=["POST"])
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def synthesize():
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# TODO Implementation with json to support more platform
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# Load synthesizer
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if "synt_path" in request.form:
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synt_path = request.form["synt_path"]
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else:
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synt_path = synthesizers[0]
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print("NO synthsizer is specified, try default first one.")
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if synthesizers_cache.get(synt_path) is None:
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current_synt = Synthesizer(Path(synt_path))
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synthesizers_cache[synt_path] = current_synt
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else:
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current_synt = synthesizers_cache[synt_path]
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print("using synthesizer model: " + str(synt_path))
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# Load input wav
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wav_base64 = request.form["upfile_b64"]
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wav = base64.b64decode(bytes(wav_base64, 'utf-8'))
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wav = pcm2float(np.frombuffer(wav, dtype=np.int16), dtype=np.float32)
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encoder_wav = encoder.preprocess_wav(wav, 16000)
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embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
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# Load input text
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texts = request.form["text"].split("\n")
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punctuation = '！，。、,' # punctuate and split/clean text
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processed_texts = []
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for text in texts:
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for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
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if processed_text:
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processed_texts.append(processed_text.strip())
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texts = processed_texts
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# synthesize and vocode
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embeds = [embed] * len(texts)
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specs = current_synt.synthesize_spectrograms(texts, embeds)
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spec = np.concatenate(specs, axis=1)
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wav = gan_vocoder.infer_waveform(spec)
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# Return cooked wav
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out = io.BytesIO()
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write(out, Synthesizer.sample_rate, wav)
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return Response(out, mimetype="audio/wav")
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@app.route('/', methods=['GET'])
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def index():
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return render_template("index.html")
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host = app.config.get("HOST")
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port = app.config.get("PORT")
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print(f"Web server: http://{host}:{port}")
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server = wsgi.WSGIServer((host, port), app)
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server.serve_forever()
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return app
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if __name__ == "__main__":
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webApp() |