Hui Zhang
5 years ago
parent
4a3768ad18
commit
a1b04176ac
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Record wav from Microphone"""
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# http://people.csail.mit.edu/hubert/pyaudio/
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import pyaudio
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import wave
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CHUNK = 1024
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FORMAT = pyaudio.paInt16
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CHANNELS = 1
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RATE = 16000
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RECORD_SECONDS = 5
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WAVE_OUTPUT_FILENAME = "output.wav"
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p = pyaudio.PyAudio()
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stream = p.open(
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format=FORMAT,
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channels=CHANNELS,
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rate=RATE,
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input=True,
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frames_per_buffer=CHUNK)
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print("* recording")
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frames = []
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for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
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data = stream.read(CHUNK)
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frames.append(data)
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print("* done recording")
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stream.stop_stream()
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stream.close()
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p.terminate()
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wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(p.get_sample_size(FORMAT))
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wf.setframerate(RATE)
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wf.writeframes(b''.join(frames))
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wf.close()
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Socket client to send wav to ASR server."""
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import struct
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import socket
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import argparse
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import wave
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--host_ip",
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default="localhost",
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type=str,
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help="Server IP address. (default: %(default)s)")
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parser.add_argument(
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"--host_port",
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default=8086,
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type=int,
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help="Server Port. (default: %(default)s)")
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args = parser.parse_args()
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WAVE_OUTPUT_FILENAME = "output.wav"
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def main():
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wf = wave.open(WAVE_OUTPUT_FILENAME, 'rb')
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nframe = wf.getnframes()
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data = wf.readframes(nframe)
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print(f"Wave: {WAVE_OUTPUT_FILENAME}")
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print(f"Wave samples: {nframe}")
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print(f"Wave channels: {wf.getnchannels()}")
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print(f"Wave sample rate: {wf.getframerate()}")
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print(f"Wave sample width: {wf.getsampwidth()}")
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assert isinstance(data, bytes)
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.connect((args.host_ip, args.host_port))
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sent = data
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sock.sendall(struct.pack('>i', len(sent)) + sent)
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print('Speech[length=%d] Sent.' % len(sent))
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# Receive data from the server and shut down
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received = sock.recv(1024)
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print("Recognition Results: {}".format(received.decode('utf8')))
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sock.close()
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if __name__ == "__main__":
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main()
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Server-end for the ASR demo."""
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import os
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import time
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import random
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import argparse
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import functools
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from time import gmtime, strftime
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import socketserver
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import struct
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import wave
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import paddle
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import numpy as np
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.exps.deepspeech2.config import get_cfg_defaults
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.utils.utility import add_arguments, print_arguments
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from deepspeech.models.deepspeech2 import DeepSpeech2Model
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from deepspeech.io.dataset import ManifestDataset
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class AsrTCPServer(socketserver.TCPServer):
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"""The ASR TCP Server."""
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def __init__(self,
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server_address,
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RequestHandlerClass,
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speech_save_dir,
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audio_process_handler,
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bind_and_activate=True):
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self.speech_save_dir = speech_save_dir
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self.audio_process_handler = audio_process_handler
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socketserver.TCPServer.__init__(
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self, server_address, RequestHandlerClass, bind_and_activate=True)
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class AsrRequestHandler(socketserver.BaseRequestHandler):
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"""The ASR request handler."""
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def handle(self):
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# receive data through TCP socket
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chunk = self.request.recv(1024)
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target_len = struct.unpack('>i', chunk[:4])[0]
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data = chunk[4:]
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while len(data) < target_len:
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chunk = self.request.recv(1024)
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data += chunk
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# write to file
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filename = self._write_to_file(data)
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print("Received utterance[length=%d] from %s, saved to %s." %
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(len(data), self.client_address[0], filename))
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start_time = time.time()
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transcript = self.server.audio_process_handler(filename)
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finish_time = time.time()
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print("Response Time: %f, Transcript: %s" %
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(finish_time - start_time, transcript))
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self.request.sendall(transcript.encode('utf-8'))
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def _write_to_file(self, data):
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# prepare save dir and filename
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if not os.path.exists(self.server.speech_save_dir):
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os.mkdir(self.server.speech_save_dir)
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timestamp = strftime("%Y%m%d%H%M%S", gmtime())
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out_filename = os.path.join(
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self.server.speech_save_dir,
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timestamp + "_" + self.client_address[0] + ".wav")
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# write to wav file
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file = wave.open(out_filename, 'wb')
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file.setnchannels(1)
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file.setsampwidth(2)
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file.setframerate(16000)
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file.writeframes(data)
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file.close()
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return out_filename
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def warm_up_test(audio_process_handler,
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manifest_path,
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num_test_cases,
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random_seed=0):
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"""Warming-up test."""
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manifest = read_manifest(manifest_path)
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rng = random.Random(random_seed)
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samples = rng.sample(manifest, num_test_cases)
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for idx, sample in enumerate(samples):
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print("Warm-up Test Case %d: %s", idx, sample['audio_filepath'])
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start_time = time.time()
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transcript = audio_process_handler(sample['audio_filepath'])
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finish_time = time.time()
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print("Response Time: %f, Transcript: %s" %
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(finish_time - start_time, transcript))
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def start_server(config, args):
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"""Start the ASR server"""
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dataset = ManifestDataset(
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config.data.test_manifest,
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config.data.vocab_filepath,
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config.data.mean_std_filepath,
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augmentation_config="{}",
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max_duration=config.data.max_duration,
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min_duration=config.data.min_duration,
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stride_ms=config.data.stride_ms,
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window_ms=config.data.window_ms,
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n_fft=config.data.n_fft,
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max_freq=config.data.max_freq,
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target_sample_rate=config.data.target_sample_rate,
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specgram_type=config.data.specgram_type,
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use_dB_normalization=config.data.use_dB_normalization,
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target_dB=config.data.target_dB,
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random_seed=config.data.random_seed,
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keep_transcription_text=True)
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model = DeepSpeech2Model(
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feat_size=dataset.feature_size,
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dict_size=dataset.vocab_size,
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num_conv_layers=config.model.num_conv_layers,
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num_rnn_layers=config.model.num_rnn_layers,
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rnn_size=config.model.rnn_layer_size,
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use_gru=config.model.use_gru,
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share_rnn_weights=config.model.share_rnn_weights)
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model.from_pretrained(args.checkpoint_path)
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model.eval()
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# prepare ASR inference handler
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def file_to_transcript(filename):
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feature = dataset.process_utterance(filename, "")
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audio = np.array([feature[0]]).astype('float32') #[1, D, T]
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audio_len = feature[0].shape[1]
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audio_len = np.array([audio_len]).astype('int64') # [1]
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result_transcript = model.decode(
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paddle.to_tensor(audio),
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paddle.to_tensor(audio_len),
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vocab_list=dataset.vocab_list,
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decoding_method=config.decoding.decoding_method,
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lang_model_path=config.decoding.lang_model_path,
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beam_alpha=config.decoding.alpha,
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beam_beta=config.decoding.beta,
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beam_size=config.decoding.beam_size,
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cutoff_prob=config.decoding.cutoff_prob,
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cutoff_top_n=config.decoding.cutoff_top_n,
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num_processes=config.decoding.num_proc_bsearch)
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return result_transcript[0]
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# warming up with utterrances sampled from Librispeech
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print('-----------------------------------------------------------')
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print('Warming up ...')
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warm_up_test(
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audio_process_handler=file_to_transcript,
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manifest_path=args.warmup_manifest,
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num_test_cases=3)
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print('-----------------------------------------------------------')
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# start the server
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server = AsrTCPServer(
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server_address=(args.host_ip, args.host_port),
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RequestHandlerClass=AsrRequestHandler,
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speech_save_dir=args.speech_save_dir,
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audio_process_handler=file_to_transcript)
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print("ASR Server Started.")
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server.serve_forever()
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def main(config, args):
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start_server(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('host_ip', str,
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'localhost',
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"Server's IP address.")
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add_arg('host_port', int, 8086, "Server's IP port.")
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add_arg('speech_save_dir', str,
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'demo_cache',
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"Directory to save demo audios.")
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add_arg('warmup_manifest', str, None, "Filepath of manifest to warm up.")
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args = parser.parse_args()
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print_arguments(args)
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# https://yaml.org/type/float.html
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config = get_cfg_defaults()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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args.warmup_manifest = config.data.test_manifest
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print_arguments(args)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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main(config, args)
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@ -1,252 +0,0 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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|
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Server-end for the ASR demo."""
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import os
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import time
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import random
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import argparse
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import functools
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from time import gmtime, strftime
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import SocketServer
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import struct
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import wave
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import paddle.fluid as fluid
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import numpy as np
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.utils.utility import add_arguments, print_arguments
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from deepspeech.exps.deepspeech2.model import DeepSpeech2Model
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from deepspeech.exps.deepspeech2.dataset import DataGenerator
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('host_port', int, 8086, "Server's IP port.")
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add_arg('beam_size', int, 500, "Beam search width.")
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('alpha', float, 2.5, "Coef of LM for beam search.")
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add_arg('beta', float, 0.3, "Coef of WC for beam search.")
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add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.")
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||||||
add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.")
|
|
||||||
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
|
|
||||||
add_arg('use_gpu', bool, True, "Use GPU or not.")
|
|
||||||
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
|
|
||||||
"bi-directional RNNs. Not for GRU.")
|
|
||||||
add_arg('host_ip', str,
|
|
||||||
'localhost',
|
|
||||||
"Server's IP address.")
|
|
||||||
add_arg('speech_save_dir', str,
|
|
||||||
'demo_cache',
|
|
||||||
"Directory to save demo audios.")
|
|
||||||
add_arg('warmup_manifest', str,
|
|
||||||
'data/librispeech/manifest.test-clean',
|
|
||||||
"Filepath of manifest to warm up.")
|
|
||||||
add_arg('mean_std_path', str,
|
|
||||||
'data/librispeech/mean_std.npz',
|
|
||||||
"Filepath of normalizer's mean & std.")
|
|
||||||
add_arg('vocab_path', str,
|
|
||||||
'data/librispeech/eng_vocab.txt',
|
|
||||||
"Filepath of vocabulary.")
|
|
||||||
add_arg('model_path', str,
|
|
||||||
'./checkpoints/libri/step_final',
|
|
||||||
"If None, the training starts from scratch, "
|
|
||||||
"otherwise, it resumes from the pre-trained model.")
|
|
||||||
add_arg('lang_model_path', str,
|
|
||||||
'lm/data/common_crawl_00.prune01111.trie.klm',
|
|
||||||
"Filepath for language model.")
|
|
||||||
add_arg('decoding_method', str,
|
|
||||||
'ctc_beam_search',
|
|
||||||
"Decoding method. Options: ctc_beam_search, ctc_greedy",
|
|
||||||
choices = ['ctc_beam_search', 'ctc_greedy'])
|
|
||||||
add_arg('specgram_type', str,
|
|
||||||
'linear',
|
|
||||||
"Audio feature type. Options: linear, mfcc.",
|
|
||||||
choices=['linear', 'mfcc'])
|
|
||||||
# yapf: disable
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
|
|
||||||
class AsrTCPServer(SocketServer.TCPServer):
|
|
||||||
"""The ASR TCP Server."""
|
|
||||||
|
|
||||||
def __init__(self,
|
|
||||||
server_address,
|
|
||||||
RequestHandlerClass,
|
|
||||||
speech_save_dir,
|
|
||||||
audio_process_handler,
|
|
||||||
bind_and_activate=True):
|
|
||||||
self.speech_save_dir = speech_save_dir
|
|
||||||
self.audio_process_handler = audio_process_handler
|
|
||||||
SocketServer.TCPServer.__init__(
|
|
||||||
self, server_address, RequestHandlerClass, bind_and_activate=True)
|
|
||||||
|
|
||||||
|
|
||||||
class AsrRequestHandler(SocketServer.BaseRequestHandler):
|
|
||||||
"""The ASR request handler."""
|
|
||||||
|
|
||||||
def handle(self):
|
|
||||||
# receive data through TCP socket
|
|
||||||
chunk = self.request.recv(1024)
|
|
||||||
target_len = struct.unpack('>i', chunk[:4])[0]
|
|
||||||
data = chunk[4:]
|
|
||||||
while len(data) < target_len:
|
|
||||||
chunk = self.request.recv(1024)
|
|
||||||
data += chunk
|
|
||||||
# write to file
|
|
||||||
filename = self._write_to_file(data)
|
|
||||||
|
|
||||||
print("Received utterance[length=%d] from %s, saved to %s." %
|
|
||||||
(len(data), self.client_address[0], filename))
|
|
||||||
start_time = time.time()
|
|
||||||
transcript = self.server.audio_process_handler(filename)
|
|
||||||
finish_time = time.time()
|
|
||||||
print("Response Time: %f, Transcript: %s" %
|
|
||||||
(finish_time - start_time, transcript))
|
|
||||||
self.request.sendall(transcript.encode('utf-8'))
|
|
||||||
|
|
||||||
def _write_to_file(self, data):
|
|
||||||
# prepare save dir and filename
|
|
||||||
if not os.path.exists(self.server.speech_save_dir):
|
|
||||||
os.mkdir(self.server.speech_save_dir)
|
|
||||||
timestamp = strftime("%Y%m%d%H%M%S", gmtime())
|
|
||||||
out_filename = os.path.join(
|
|
||||||
self.server.speech_save_dir,
|
|
||||||
timestamp + "_" + self.client_address[0] + ".wav")
|
|
||||||
# write to wav file
|
|
||||||
file = wave.open(out_filename, 'wb')
|
|
||||||
file.setnchannels(1)
|
|
||||||
file.setsampwidth(4)
|
|
||||||
file.setframerate(16000)
|
|
||||||
file.writeframes(data)
|
|
||||||
file.close()
|
|
||||||
return out_filename
|
|
||||||
|
|
||||||
|
|
||||||
def warm_up_test(audio_process_handler,
|
|
||||||
manifest_path,
|
|
||||||
num_test_cases,
|
|
||||||
random_seed=0):
|
|
||||||
"""Warming-up test."""
|
|
||||||
manifest = read_manifest(manifest_path)
|
|
||||||
rng = random.Random(random_seed)
|
|
||||||
samples = rng.sample(manifest, num_test_cases)
|
|
||||||
for idx, sample in enumerate(samples):
|
|
||||||
print("Warm-up Test Case %d: %s", idx, sample['audio_filepath'])
|
|
||||||
start_time = time.time()
|
|
||||||
transcript = audio_process_handler(sample['audio_filepath'])
|
|
||||||
finish_time = time.time()
|
|
||||||
print("Response Time: %f, Transcript: %s" %
|
|
||||||
(finish_time - start_time, transcript))
|
|
||||||
|
|
||||||
|
|
||||||
def start_server():
|
|
||||||
"""Start the ASR server"""
|
|
||||||
# prepare data generator
|
|
||||||
if args.use_gpu:
|
|
||||||
place = fluid.CUDAPlace(0)
|
|
||||||
else:
|
|
||||||
place = fluid.CPUPlace()
|
|
||||||
|
|
||||||
data_generator = DataGenerator(
|
|
||||||
vocab_filepath=args.vocab_path,
|
|
||||||
mean_std_filepath=args.mean_std_path,
|
|
||||||
augmentation_config='{}',
|
|
||||||
specgram_type=args.specgram_type,
|
|
||||||
keep_transcription_text=True,
|
|
||||||
place = place,
|
|
||||||
is_training = False)
|
|
||||||
# prepare ASR model
|
|
||||||
ds2_model = DeepSpeech2Model(
|
|
||||||
vocab_size=data_generator.vocab_size,
|
|
||||||
num_conv_layers=args.num_conv_layers,
|
|
||||||
num_rnn_layers=args.num_rnn_layers,
|
|
||||||
rnn_layer_size=args.rnn_layer_size,
|
|
||||||
use_gru=args.use_gru,
|
|
||||||
init_from_pretrained_model=args.model_path,
|
|
||||||
place=place,
|
|
||||||
share_rnn_weights=args.share_rnn_weights)
|
|
||||||
|
|
||||||
vocab_list = [chars for chars in data_generator.vocab_list]
|
|
||||||
|
|
||||||
if args.decoding_method == "ctc_beam_search":
|
|
||||||
ds2_model.init_ext_scorer(args.alpha, args.beta, args.lang_model_path,
|
|
||||||
vocab_list)
|
|
||||||
# prepare ASR inference handler
|
|
||||||
def file_to_transcript(filename):
|
|
||||||
feature = data_generator.process_utterance(filename, "")
|
|
||||||
audio_len = feature[0].shape[1]
|
|
||||||
mask_shape0 = (feature[0].shape[0] - 1) // 2 + 1
|
|
||||||
mask_shape1 = (feature[0].shape[1] - 1) // 3 + 1
|
|
||||||
mask_max_len = (audio_len - 1) // 3 + 1
|
|
||||||
mask_ones = np.ones((mask_shape0, mask_shape1))
|
|
||||||
mask_zeros = np.zeros((mask_shape0, mask_max_len - mask_shape1))
|
|
||||||
mask = np.repeat(
|
|
||||||
np.reshape(
|
|
||||||
np.concatenate((mask_ones, mask_zeros), axis=1),
|
|
||||||
(1, mask_shape0, mask_max_len)),
|
|
||||||
32,
|
|
||||||
axis=0)
|
|
||||||
feature = (np.array([feature[0]]).astype('float32'),
|
|
||||||
None,
|
|
||||||
np.array([audio_len]).astype('int64').reshape([-1,1]),
|
|
||||||
np.array([mask]).astype('float32'))
|
|
||||||
probs_split = ds2_model.infer_batch_probs(
|
|
||||||
infer_data=feature,
|
|
||||||
feeding_dict=data_generator.feeding)
|
|
||||||
|
|
||||||
if args.decoding_method == "ctc_greedy":
|
|
||||||
result_transcript = ds2_model.decode_batch_greedy(
|
|
||||||
probs_split=probs_split,
|
|
||||||
vocab_list=vocab_list)
|
|
||||||
else:
|
|
||||||
result_transcript = ds2_model.decode_batch_beam_search(
|
|
||||||
probs_split=probs_split,
|
|
||||||
beam_alpha=args.alpha,
|
|
||||||
beam_beta=args.beta,
|
|
||||||
beam_size=args.beam_size,
|
|
||||||
cutoff_prob=args.cutoff_prob,
|
|
||||||
cutoff_top_n=args.cutoff_top_n,
|
|
||||||
vocab_list=vocab_list,
|
|
||||||
num_processes=1)
|
|
||||||
return result_transcript[0]
|
|
||||||
|
|
||||||
# warming up with utterrances sampled from Librispeech
|
|
||||||
print('-----------------------------------------------------------')
|
|
||||||
print('Warming up ...')
|
|
||||||
warm_up_test(
|
|
||||||
audio_process_handler=file_to_transcript,
|
|
||||||
manifest_path=args.warmup_manifest,
|
|
||||||
num_test_cases=3)
|
|
||||||
print('-----------------------------------------------------------')
|
|
||||||
|
|
||||||
# start the server
|
|
||||||
server = AsrTCPServer(
|
|
||||||
server_address=(args.host_ip, args.host_port),
|
|
||||||
RequestHandlerClass=AsrRequestHandler,
|
|
||||||
speech_save_dir=args.speech_save_dir,
|
|
||||||
audio_process_handler=file_to_transcript)
|
|
||||||
print("ASR Server Started.")
|
|
||||||
server.serve_forever()
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
print_arguments(args)
|
|
||||||
start_server()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -1,2 +1,4 @@
|
|||||||
data
|
data
|
||||||
ckpt*
|
ckpt*
|
||||||
|
demo_cache
|
||||||
|
*.log
|
||||||
|
|||||||
@ -0,0 +1,40 @@
|
|||||||
|
#! /usr/bin/env bash
|
||||||
|
# TODO: replace the model with a mandarin model
|
||||||
|
|
||||||
|
if [[ $# != 1 ]];then
|
||||||
|
echo "usage: server.sh checkpoint_path"
|
||||||
|
exit -1
|
||||||
|
fi
|
||||||
|
|
||||||
|
source path.sh
|
||||||
|
|
||||||
|
# download language model
|
||||||
|
bash local/download_lm_ch.sh
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# download well-trained model
|
||||||
|
bash local/download_model.sh
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# start demo server
|
||||||
|
CUDA_VISIBLE_DEVICES=0 \
|
||||||
|
python3 -u ${BIN_DIR}/deploy/server.py \
|
||||||
|
--device 'gpu' \
|
||||||
|
--nproc 1 \
|
||||||
|
--config conf/deepspeech2.yaml \
|
||||||
|
--host_ip="localhost" \
|
||||||
|
--host_port=8086 \
|
||||||
|
--speech_save_dir="demo_cache" \
|
||||||
|
--checkpoint_path ${1}
|
||||||
|
|
||||||
|
if [ $? -ne 0 ]; then
|
||||||
|
echo "Failed in starting demo server!"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
|
||||||
|
exit 0
|
||||||
@ -1,8 +0,0 @@
|
|||||||
export MAIN_ROOT=${PWD}/../../
|
|
||||||
|
|
||||||
export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
|
|
||||||
export LC_ALL=C
|
|
||||||
|
|
||||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
|
||||||
export PYTHONIOENCODING=UTF-8
|
|
||||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
|
||||||
@ -1,54 +0,0 @@
|
|||||||
#! /usr/bin/env bash
|
|
||||||
# TODO: replace the model with a mandarin model
|
|
||||||
|
|
||||||
source path.sh
|
|
||||||
|
|
||||||
# download language model
|
|
||||||
cd ${MAIN_ROOT}/models/lm > /dev/null
|
|
||||||
bash download_lm_en.sh
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
cd - > /dev/null
|
|
||||||
|
|
||||||
|
|
||||||
# download well-trained model
|
|
||||||
cd ${MAIN_ROOT}/models/baidu_en8k > /dev/null
|
|
||||||
bash download_model.sh
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
cd - > /dev/null
|
|
||||||
|
|
||||||
|
|
||||||
# start demo server
|
|
||||||
CUDA_VISIBLE_DEVICES=0 \
|
|
||||||
python3 -u ${MAIN_ROOT}/deploy/demo_server.py \
|
|
||||||
--host_ip="localhost" \
|
|
||||||
--host_port=8086 \
|
|
||||||
--num_conv_layers=2 \
|
|
||||||
--num_rnn_layers=3 \
|
|
||||||
--rnn_layer_size=1024 \
|
|
||||||
--alpha=1.15 \
|
|
||||||
--beta=0.15 \
|
|
||||||
--cutoff_prob=1.0 \
|
|
||||||
--cutoff_top_n=40 \
|
|
||||||
--use_gru=True \
|
|
||||||
--use_gpu=True \
|
|
||||||
--share_rnn_weights=False \
|
|
||||||
--speech_save_dir="demo_cache" \
|
|
||||||
--warmup_manifest="${MAIN_ROOT}/examples/tiny/data/manifest.test-clean" \
|
|
||||||
--mean_std_path="${MAIN_ROOT}/models/baidu_en8k/mean_std.npz" \
|
|
||||||
--vocab_path="${MAIN_ROOT}/models/baidu_en8k/vocab.txt" \
|
|
||||||
--model_path="${MAIN_ROOT}/models/baidu_en8k" \
|
|
||||||
--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
|
|
||||||
--decoding_method="ctc_beam_search" \
|
|
||||||
--specgram_type="linear"
|
|
||||||
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed in starting demo server!"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
|
|
||||||
exit 0
|
|
||||||
Loading…
Reference in new issue