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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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update the auto_log

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pull/1014/head
huangyuxin 3 years ago
parent d2d60624bd
commit fb6974f950
2 changed files with 66 additions and 26 deletions
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2
paddlespeech/s2t/exps/deepspeech2/bin/test_export.py
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@ -39,6 +39,8 @@ if __name__ == "__main__":
"--export_path", type=str, help="path of the jit model to save")
parser.add_argument(
"--model_type", type=str, default='offline', help='offline/online')
parser.add_argument(
"--enable-auto-log", action="store_true", help="use auto log")
args = parser.parse_args()
print_arguments(args, globals())
print("model_type:{}".format(args.model_type))

90
paddlespeech/s2t/exps/deepspeech2/model.py
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@ -42,7 +42,6 @@ from paddlespeech.s2t.training.trainer import Trainer
from paddlespeech.s2t.utils import error_rate
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.log import Autolog
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import UpdateConfig
@ -339,8 +338,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
error_rate_type=cfg.error_rate_type)
def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
self.autolog.times.start()
self.autolog.times.stamp()
result_transcripts = self.model.decode(
audio,
audio_len,
@ -354,19 +351,12 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
cutoff_top_n=cfg.cutoff_top_n,
num_processes=cfg.num_proc_bsearch)
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
return result_transcripts
@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
@ -390,7 +380,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
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()
@paddle.no_grad()
def export(self):
@ -414,6 +403,43 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
def __init__(self, config, args):
super().__init__(config, args)
self.apply_static = True
self.args = args
@mp_tools.rank_zero_only
@paddle.no_grad()
def test(self):
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
if self.args.enable_auto_log == True:
from paddlespeech.s2t.utils.log import Autolog
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 jsonlines.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)
if self.args.enable_auto_log == True:
self.autolog.report()
def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
if self.args.model_type == "online":
@ -486,8 +512,8 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
x_len_list = np.split(x_len_batch, batch_size, axis=0)
for x, x_len in zip(x_list, x_len_list):
self.autolog.times.start()
self.autolog.times.stamp()
if self.args.enable_auto_log == True:
self.autolog.times.start()
x_len = x_len[0]
assert (chunk_size <= x_len)
@ -521,6 +547,10 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
probs_chunk_list = []
probs_chunk_lens_list = []
if self.args.enable_auto_log == True:
# record the model preprocessing time
self.autolog.times.stamp()
for i in range(0, num_chunk):
start = i * chunk_stride
end = start + chunk_size
@ -576,9 +606,12 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
[output_probs, output_probs_padding], axis=1)
output_probs_list.append(output_probs)
output_lens_list.append(output_lens)
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
if self.args.enable_auto_log == True:
# record the model inference time
self.autolog.times.stamp()
# record the post processing time
self.autolog.times.stamp()
self.autolog.times.end()
output_probs = np.concatenate(output_probs_list, axis=0)
output_lens = np.concatenate(output_lens_list, axis=0)
return output_probs, output_lens
@ -608,12 +641,17 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
audio_len_handle.reshape(x_len.shape)
audio_len_handle.copy_from_cpu(x_len)
self.autolog.times.start()
self.autolog.times.stamp()
if self.args.enable_auto_log == True:
self.autolog.times.start()
# record the prefix processing time
self.autolog.times.stamp()
self.predictor.run()
self.autolog.times.stamp()
self.autolog.times.stamp()
self.autolog.times.end()
if self.args.enable_auto_log == True:
# record the model inference time
self.autolog.times.stamp()
# record the post processing time
self.autolog.times.stamp()
self.autolog.times.end()
output_names = self.predictor.get_output_names()
output_handle = self.predictor.get_output_handle(output_names[0])
@ -624,11 +662,11 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
def setup_model(self):
super().setup_model()
speedyspeech_config = inference.Config(
deepspeech_config = inference.Config(
self.args.export_path + ".pdmodel",
self.args.export_path + ".pdiparams")
if (os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
speedyspeech_config.enable_use_gpu(100, 0)
speedyspeech_config.enable_memory_optim()
speedyspeech_predictor = inference.create_predictor(speedyspeech_config)
self.predictor = speedyspeech_predictor
deepspeech_config.enable_use_gpu(100, 0)
deepspeech_config.enable_memory_optim()
deepspeech_predictor = inference.create_predictor(deepspeech_config)
self.predictor = deepspeech_predictor

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