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@ -20,6 +20,7 @@ from typing import Optional
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import numpy as np
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import numpy as np
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import paddle
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import paddle
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from paddle import distributed as dist
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from paddle import distributed as dist
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from paddle import inference
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from paddle.io import DataLoader
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from paddle.io import DataLoader
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from yacs.config import CfgNode
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from yacs.config import CfgNode
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@ -145,7 +146,7 @@ class DeepSpeech2Trainer(Trainer):
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learning_rate=config.training.lr,
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learning_rate=config.training.lr,
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gamma=config.training.lr_decay,
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gamma=config.training.lr_decay,
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verbose=True)
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verbose=True)
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optimizer = paddle.optimizer.Adam(
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optimizer = paddle.optimizer.SGD( #Adam
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learning_rate=lr_scheduler,
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learning_rate=lr_scheduler,
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parameters=model.parameters(),
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parameters=model.parameters(),
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weight_decay=paddle.regularizer.L2Decay(
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weight_decay=paddle.regularizer.L2Decay(
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@ -395,3 +396,332 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
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output_dir.mkdir(parents=True, exist_ok=True)
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output_dir.mkdir(parents=True, exist_ok=True)
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self.output_dir = output_dir
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self.output_dir = output_dir
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class DeepSpeech2ExportTester(DeepSpeech2Trainer):
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# testing config
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default = CfgNode(
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dict(
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alpha=2.5, # Coef of LM for beam search.
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beta=0.3, # Coef of WC for beam search.
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cutoff_prob=1.0, # Cutoff probability for pruning.
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cutoff_top_n=40, # Cutoff number for pruning.
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lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model.
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decoding_method='ctc_beam_search', # Decoding method. Options: ctc_beam_search, ctc_greedy
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error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer'
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num_proc_bsearch=8, # # of CPUs for beam search.
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beam_size=500, # Beam search width.
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batch_size=128, # decoding batch size
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))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self, config, args):
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super().__init__(config, args)
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def ordid2token(self, texts, texts_len):
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""" ord() id to chr() chr """
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trans = []
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for text, n in zip(texts, texts_len):
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n = n.numpy().item()
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ids = text[:n]
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trans.append(''.join([chr(i) for i in ids]))
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return trans
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def compute_metrics(self,
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utts,
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audio,
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audio_len,
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texts,
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texts_len,
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fout=None):
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cfg = self.config.decoding
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errors_sum, len_refs, num_ins = 0.0, 0, 0
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errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
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error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
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vocab_list = self.test_loader.collate_fn.vocab_list
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batch_size = self.config.decoding.batch_size
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output_prob_list = []
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output_lens_list = []
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decoder_chunk_size = 8
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subsampling_rate = self.model.encoder.conv.subsampling_rate
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receptive_field_length = self.model.encoder.conv.receptive_field_length
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chunk_stride = subsampling_rate * decoder_chunk_size
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chunk_size = (decoder_chunk_size - 1
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) * subsampling_rate + receptive_field_length
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x_batch = audio.numpy()
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x_len_batch = audio_len.numpy().astype(np.int64)
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max_len_batch = x_batch.shape[1]
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batch_padding_len = chunk_stride - (
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max_len_batch - chunk_size
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) % chunk_stride # The length of padding for the batch
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x_list = np.split(x_batch, x_batch.shape[0], axis=0)
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x_len_list = np.split(x_len_batch, x_batch.shape[0], axis=0)
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for x, x_len in zip(x_list, x_len_list):
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assert (chunk_size <= x_len[0])
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eouts_chunk_list = []
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eouts_chunk_lens_list = []
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padding_len_x = chunk_stride - (x_len[0] - chunk_size
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) % chunk_stride
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padding = np.zeros(
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(x.shape[0], padding_len_x, x.shape[2]), dtype=np.float32)
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padded_x = np.concatenate([x, padding], axis=1)
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num_chunk = (x_len[0] + padding_len_x - chunk_size
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) / chunk_stride + 1
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num_chunk = int(num_chunk)
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chunk_state_h_box = np.zeros(
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(self.config.model.num_rnn_layers, 1,
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self.config.model.rnn_layer_size),
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dtype=np.float32)
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chunk_state_c_box = np.zeros(
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(self.config.model.num_rnn_layers, 1,
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self.config.model.rnn_layer_size),
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dtype=np.float32)
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input_names = self.predictor.get_input_names()
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audio_handle = self.predictor.get_input_handle(input_names[0])
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audio_len_handle = self.predictor.get_input_handle(input_names[1])
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h_box_handle = self.predictor.get_input_handle(input_names[2])
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c_box_handle = self.predictor.get_input_handle(input_names[3])
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probs_chunk_list = []
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probs_chunk_lens_list = []
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for i in range(0, num_chunk):
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start = i * chunk_stride
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end = start + chunk_size
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x_chunk = padded_x[:, start:end, :]
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x_len_left = np.where(x_len - i * chunk_stride < 0,
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np.zeros_like(x_len, dtype=np.int64),
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x_len - i * chunk_stride)
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x_chunk_len_tmp = np.ones_like(
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x_len, dtype=np.int64) * chunk_size
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x_chunk_lens = np.where(x_len_left < x_chunk_len_tmp,
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x_len_left, x_chunk_len_tmp)
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if (x_chunk_lens[0] <
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receptive_field_length): #means the number of input frames in the chunk is not enough for predicting one prob
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break
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audio_handle.reshape(x_chunk.shape)
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audio_handle.copy_from_cpu(x_chunk)
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audio_len_handle.reshape(x_chunk_lens.shape)
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audio_len_handle.copy_from_cpu(x_chunk_lens)
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h_box_handle.reshape(chunk_state_h_box.shape)
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h_box_handle.copy_from_cpu(chunk_state_h_box)
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c_box_handle.reshape(chunk_state_c_box.shape)
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c_box_handle.copy_from_cpu(chunk_state_c_box)
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output_names = self.predictor.get_output_names()
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output_handle = self.predictor.get_output_handle(
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output_names[0])
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output_lens_handle = self.predictor.get_output_handle(
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output_names[1])
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output_state_h_handle = self.predictor.get_output_handle(
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output_names[2])
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output_state_c_handle = self.predictor.get_output_handle(
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output_names[3])
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self.predictor.run()
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output_chunk_prob = output_handle.copy_to_cpu()
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output_chunk_lens = output_lens_handle.copy_to_cpu()
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chunk_state_h_box = output_state_h_handle.copy_to_cpu()
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chunk_state_c_box = output_state_c_handle.copy_to_cpu()
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output_chunk_prob = paddle.to_tensor(output_chunk_prob)
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output_chunk_lens = paddle.to_tensor(output_chunk_lens)
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probs_chunk_list.append(output_chunk_prob)
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probs_chunk_lens_list.append(output_chunk_lens)
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output_prob = paddle.concat(probs_chunk_list, axis=1)
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output_lens = paddle.add_n(probs_chunk_lens_list)
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output_prob_padding_len = max_len_batch + batch_padding_len - output_prob.shape[
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1]
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output_prob_padding = paddle.zeros(
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(1, output_prob_padding_len, output_prob.shape[2]),
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dtype="float32") # The prob padding for a piece of utterance
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output_prob = paddle.concat(
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[output_prob, output_prob_padding], axis=1)
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output_prob_list.append(output_prob)
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output_lens_list.append(output_lens)
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output_prob_branch = paddle.concat(output_prob_list, axis=0)
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output_lens_branch = paddle.concat(output_lens_list, axis=0)
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"""
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x = audio.numpy()
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x_len = audio_len.numpy().astype(np.int64)
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input_names = self.predictor.get_input_names()
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audio_handle = self.predictor.get_input_handle(input_names[0])
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audio_len_handle = self.predictor.get_input_handle(input_names[1])
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h_box_handle = self.predictor.get_input_handle(input_names[2])
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c_box_handle = self.predictor.get_input_handle(input_names[3])
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audio_handle.reshape(x.shape)
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audio_handle.copy_from_cpu(x)
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audio_len_handle.reshape(x_len.shape)
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audio_len_handle.copy_from_cpu(x_len)
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init_state_h_box = np.zeros((self.config.model.num_rnn_layers, audio.shape[0], self.config.model.rnn_layer_size), dtype=np.float32)
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init_state_c_box = np.zeros((self.config.model.num_rnn_layers, audio.shape[0], self.config.model.rnn_layer_size), dtype=np.float32)
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h_box_handle.reshape(init_state_h_box.shape)
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h_box_handle.copy_from_cpu(init_state_h_box)
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c_box_handle.reshape(init_state_c_box.shape)
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c_box_handle.copy_from_cpu(init_state_c_box)
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#self.autolog.times.start()
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#self.autolog.times.stamp()
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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output_handle = self.predictor.get_output_handle(output_names[0])
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output_lens_handle = self.predictor.get_output_handle(output_names[1])
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output_state_h_handle = self.predictor.get_output_handle(output_names[2])
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output_state_c_handle = self.predictor.get_output_handle(output_names[3])
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output_prob = output_handle.copy_to_cpu()
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output_lens = output_lens_handle.copy_to_cpu()
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output_stata_h_box = output_state_h_handle.copy_to_cpu()
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output_stata_c_box = output_state_c_handle.copy_to_cpu()
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output_prob_branch = paddle.to_tensor(output_prob)
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output_lens_branch = paddle.to_tensor(output_lens)
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"""
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result_transcripts = self.model.decode_by_probs(
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output_prob_branch,
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output_lens_branch,
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vocab_list,
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decoding_method=cfg.decoding_method,
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lang_model_path=cfg.lang_model_path,
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beam_alpha=cfg.alpha,
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beam_beta=cfg.beta,
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beam_size=cfg.beam_size,
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cutoff_prob=cfg.cutoff_prob,
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cutoff_top_n=cfg.cutoff_top_n,
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num_processes=cfg.num_proc_bsearch)
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#self.autolog.times.stamp()
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#self.autolog.times.stamp()
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#self.autolog.times.end()
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target_transcripts = self.ordid2token(texts, texts_len)
|
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for utt, target, result in zip(utts, target_transcripts,
|
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|
result_transcripts):
|
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errors, len_ref = errors_func(target, result)
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|
errors_sum += errors
|
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len_refs += len_ref
|
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|
num_ins += 1
|
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if fout:
|
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|
|
fout.write(utt + " " + result + "\n")
|
|
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|
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|
|
|
logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
|
|
|
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|
(target, result))
|
|
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|
|
|
logger.info("Current error rate [%s] = %f" %
|
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|
|
|
|
|
|
(cfg.error_rate_type, error_rate_func(target, result)))
|
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|
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|
|
|
|
|
|
|
|
return dict(
|
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|
|
|
|
|
|
errors_sum=errors_sum,
|
|
|
|
|
|
|
|
len_refs=len_refs,
|
|
|
|
|
|
|
|
num_ins=num_ins,
|
|
|
|
|
|
|
|
error_rate=errors_sum / len_refs,
|
|
|
|
|
|
|
|
error_rate_type=cfg.error_rate_type)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@mp_tools.rank_zero_only
|
|
|
|
|
|
|
|
@paddle.no_grad()
|
|
|
|
|
|
|
|
def test(self):
|
|
|
|
|
|
|
|
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
|
|
|
|
|
|
|
|
#self.autolog = Autolog(
|
|
|
|
|
|
|
|
# batch_size=self.config.decoding.batch_size,
|
|
|
|
|
|
|
|
# model_name="deepspeech2",
|
|
|
|
|
|
|
|
# model_precision="fp32").getlog()
|
|
|
|
|
|
|
|
self.model.eval()
|
|
|
|
|
|
|
|
cfg = self.config
|
|
|
|
|
|
|
|
error_rate_type = None
|
|
|
|
|
|
|
|
errors_sum, len_refs, num_ins = 0.0, 0, 0
|
|
|
|
|
|
|
|
with open(self.args.result_file, 'w') as fout:
|
|
|
|
|
|
|
|
for i, batch in enumerate(self.test_loader):
|
|
|
|
|
|
|
|
utts, audio, audio_len, texts, texts_len = batch
|
|
|
|
|
|
|
|
metrics = self.compute_metrics(utts, audio, audio_len, texts,
|
|
|
|
|
|
|
|
texts_len, fout)
|
|
|
|
|
|
|
|
errors_sum += metrics['errors_sum']
|
|
|
|
|
|
|
|
len_refs += metrics['len_refs']
|
|
|
|
|
|
|
|
num_ins += metrics['num_ins']
|
|
|
|
|
|
|
|
error_rate_type = metrics['error_rate_type']
|
|
|
|
|
|
|
|
logger.info("Error rate [%s] (%d/?) = %f" %
|
|
|
|
|
|
|
|
(error_rate_type, num_ins, errors_sum / len_refs))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# logging
|
|
|
|
|
|
|
|
msg = "Test: "
|
|
|
|
|
|
|
|
msg += "epoch: {}, ".format(self.epoch)
|
|
|
|
|
|
|
|
msg += "step: {}, ".format(self.iteration)
|
|
|
|
|
|
|
|
msg += "Final error rate [%s] (%d/%d) = %f" % (
|
|
|
|
|
|
|
|
error_rate_type, num_ins, num_ins, errors_sum / len_refs)
|
|
|
|
|
|
|
|
logger.info(msg)
|
|
|
|
|
|
|
|
#self.autolog.report()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def run_test(self):
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
self.test()
|
|
|
|
|
|
|
|
except KeyboardInterrupt:
|
|
|
|
|
|
|
|
exit(-1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def run_export(self):
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
self.export()
|
|
|
|
|
|
|
|
except KeyboardInterrupt:
|
|
|
|
|
|
|
|
exit(-1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def setup(self):
|
|
|
|
|
|
|
|
"""Setup the experiment.
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
paddle.set_device(self.args.device)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.setup_output_dir()
|
|
|
|
|
|
|
|
#self.setup_checkpointer()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.setup_dataloader()
|
|
|
|
|
|
|
|
self.setup_model()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.iteration = 0
|
|
|
|
|
|
|
|
self.epoch = 0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def setup_output_dir(self):
|
|
|
|
|
|
|
|
"""Create a directory used for output.
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
# output dir
|
|
|
|
|
|
|
|
if self.args.output:
|
|
|
|
|
|
|
|
output_dir = Path(self.args.output).expanduser()
|
|
|
|
|
|
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
output_dir = Path(self.args.export_path).expanduser().parent.parent
|
|
|
|
|
|
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.output_dir = output_dir
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def setup_model(self):
|
|
|
|
|
|
|
|
super().setup_model()
|
|
|
|
|
|
|
|
if self.args.model_type == 'online':
|
|
|
|
|
|
|
|
#inference_dir = "exp/deepspeech2_online/checkpoints/"
|
|
|
|
|
|
|
|
#inference_dir = "exp/deepspeech2_online_3rr_1fc_lr_decay0.91_lstm/checkpoints/"
|
|
|
|
|
|
|
|
#speedyspeech_config = inference.Config(
|
|
|
|
|
|
|
|
# str(Path(inference_dir) / "avg_1.jit.pdmodel"),
|
|
|
|
|
|
|
|
# str(Path(inference_dir) / "avg_1.jit.pdiparams"))
|
|
|
|
|
|
|
|
speedyspeech_config = inference.Config(
|
|
|
|
|
|
|
|
self.args.export_path + ".pdmodel",
|
|
|
|
|
|
|
|
self.args.export_path + ".pdiparams")
|
|
|
|
|
|
|
|
speedyspeech_config.enable_use_gpu(100, 0)
|
|
|
|
|
|
|
|
speedyspeech_config.enable_memory_optim()
|
|
|
|
|
|
|
|
speedyspeech_predictor = inference.create_predictor(
|
|
|
|
|
|
|
|
speedyspeech_config)
|
|
|
|
|
|
|
|
self.predictor = speedyspeech_predictor
|
|
|
|