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206 lines
7.5 KiB
206 lines
7.5 KiB
"""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.v2 as paddle
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import _init_paths
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from data_utils.data import DataGenerator
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from model_utils.model import DeepSpeech2Model
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from data_utils.utility import read_manifest
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from utils.utility import add_arguments, print_arguments
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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, 2.15, "Coef of LM for beam search.")
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add_arg('beta', float, 0.35, "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.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
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"bi-directional RNNs. Not for GRU.")
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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('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,
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'data/librispeech/manifest.test-clean',
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"Filepath of manifest to warm up.")
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add_arg('mean_std_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'data/librispeech/eng_vocab.txt',
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"Filepath of vocabulary.")
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add_arg('model_path', str,
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'./checkpoints/libri/params.latest.tar.gz',
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('lang_model_path', str,
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'lm/data/common_crawl_00.prune01111.trie.klm',
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"Filepath for language model.")
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add_arg('decoding_method', str,
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'ctc_beam_search',
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"Decoding method. Options: ctc_beam_search, ctc_greedy",
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choices = ['ctc_beam_search', 'ctc_greedy'])
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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# yapf: disable
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args = parser.parse_args()
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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(4)
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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():
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"""Start the ASR server"""
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# prepare data generator
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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num_threads=1,
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keep_transcription_text=True)
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# prepare ASR model
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ds2_model = DeepSpeech2Model(
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vocab_size=data_generator.vocab_size,
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_layer_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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pretrained_model_path=args.model_path,
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share_rnn_weights=args.share_rnn_weights)
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vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
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# prepare ASR inference handler
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def file_to_transcript(filename):
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feature = data_generator.process_utterance(filename, "")
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result_transcript = ds2_model.infer_batch(
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infer_data=[feature],
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decoding_method=args.decoding_method,
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beam_alpha=args.alpha,
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beam_beta=args.beta,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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cutoff_top_n=args.cutoff_top_n,
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vocab_list=vocab_list,
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language_model_path=args.lang_model_path,
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num_processes=1)
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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():
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print_arguments(args)
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paddle.init(use_gpu=args.use_gpu, trainer_count=1)
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start_server()
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if __name__ == "__main__":
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main()
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