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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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spec augment

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pull/673/head
Haoxin Ma 4 years ago
parent ae566f667b 718bd30765
commit 1ec93dbdc4
18 changed files with 150 additions and 121 deletions
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25
deepspeech/exps/deepspeech2/bin/deploy/runtime.py
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@ -18,8 +18,10 @@ import numpy as np
import paddle
from paddle.inference import Config
from paddle.inference import create_predictor
from paddle.io import DataLoader
from deepspeech.exps.deepspeech2.config import get_cfg_defaults
from deepspeech.io.collator import SpeechCollator
from deepspeech.io.dataset import ManifestDataset
from deepspeech.models.deepspeech2 import DeepSpeech2Model
from deepspeech.training.cli import default_argument_parser
@ -78,26 +80,31 @@ def inference(config, args):
def start_server(config, args):
"""Start the ASR server"""
config.defrost()
config.data.manfiest = config.data.test_manifest
config.data.augmentation_config = ""
config.data.keep_transcription_text = True
config.data.manifest = config.data.test_manifest
dataset = ManifestDataset.from_config(config)
model = DeepSpeech2Model.from_pretrained(dataset, config,
config.collator.augmentation_config = ""
config.collator.keep_transcription_text = True
config.collator.batch_size = 1
config.collator.num_workers = 0
collate_fn = SpeechCollator.from_config(config)
test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
model = DeepSpeech2Model.from_pretrained(test_loader, config,
args.checkpoint_path)
model.eval()
# prepare ASR inference handler
def file_to_transcript(filename):
feature = dataset.process_utterance(filename, "")
audio = np.array([feature[0]]).astype('float32') #[1, D, T]
audio_len = feature[0].shape[1]
feature = test_loader.collate_fn.process_utterance(filename, "")
audio = np.array([feature[0]]).astype('float32') #[1, T, D]
audio_len = feature[0].shape[0]
audio_len = np.array([audio_len]).astype('int64') # [1]
result_transcript = model.decode(
paddle.to_tensor(audio),
paddle.to_tensor(audio_len),
vocab_list=dataset.vocab_list,
vocab_list=test_loader.collate_fn.vocab_list,
decoding_method=config.decoding.decoding_method,
lang_model_path=config.decoding.lang_model_path,
beam_alpha=config.decoding.alpha,
@ -138,7 +145,7 @@ if __name__ == "__main__":
add_arg('host_ip', str,
'localhost',
"Server's IP address.")
add_arg('host_port', int, 8086, "Server's IP port.")
add_arg('host_port', int, 8089, "Server's IP port.")
add_arg('speech_save_dir', str,
'demo_cache',
"Directory to save demo audios.")

29
deepspeech/exps/deepspeech2/bin/deploy/server.py
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@ -16,8 +16,10 @@ import functools
import numpy as np
import paddle
from paddle.io import DataLoader
from deepspeech.exps.deepspeech2.config import get_cfg_defaults
from deepspeech.io.collator import SpeechCollator
from deepspeech.io.dataset import ManifestDataset
from deepspeech.models.deepspeech2 import DeepSpeech2Model
from deepspeech.training.cli import default_argument_parser
@ -31,26 +33,35 @@ from deepspeech.utils.utility import print_arguments
def start_server(config, args):
"""Start the ASR server"""
config.defrost()
config.data.manfiest = config.data.test_manifest
config.data.augmentation_config = ""
config.data.keep_transcription_text = True
config.data.manifest = config.data.test_manifest
dataset = ManifestDataset.from_config(config)
model = DeepSpeech2Model.from_pretrained(dataset, config,
config.collator.augmentation_config = ""
config.collator.keep_transcription_text = True
config.collator.batch_size = 1
config.collator.num_workers = 0
collate_fn = SpeechCollator.from_config(config)
test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
model = DeepSpeech2Model.from_pretrained(test_loader, config,
args.checkpoint_path)
model.eval()
# prepare ASR inference handler
def file_to_transcript(filename):
feature = dataset.process_utterance(filename, "")
audio = np.array([feature[0]]).astype('float32') #[1, D, T]
audio_len = feature[0].shape[1]
feature = test_loader.collate_fn.process_utterance(filename, "")
audio = np.array([feature[0]]).astype('float32') #[1, T, D]
# audio = audio.swapaxes(1,2)
print('---file_to_transcript feature----')
print(audio.shape)
audio_len = feature[0].shape[0]
print(audio_len)
audio_len = np.array([audio_len]).astype('int64') # [1]
result_transcript = model.decode(
paddle.to_tensor(audio),
paddle.to_tensor(audio_len),
vocab_list=dataset.vocab_list,
vocab_list=test_loader.collate_fn.vocab_list,
decoding_method=config.decoding.decoding_method,
lang_model_path=config.decoding.lang_model_path,
beam_alpha=config.decoding.alpha,
@ -91,7 +102,7 @@ if __name__ == "__main__":
add_arg('host_ip', str,
'localhost',
"Server's IP address.")
add_arg('host_port', int, 8086, "Server's IP port.")
add_arg('host_port', int, 8088, "Server's IP port.")
add_arg('speech_save_dir', str,
'demo_cache',
"Directory to save demo audios.")

2
deepspeech/exps/deepspeech2/bin/tune.py
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@ -47,7 +47,7 @@ def tune(config, args):
drop_last=False,
collate_fn=SpeechCollator(keep_transcription_text=True))
model = DeepSpeech2Model.from_pretrained(dev_dataset, config,
model = DeepSpeech2Model.from_pretrained(valid_loader, config,
args.checkpoint_path)
model.eval()

2
deepspeech/exps/deepspeech2/model.py
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@ -318,7 +318,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
def export(self):
infer_model = DeepSpeech2InferModel.from_pretrained(
self.test_loader.dataset, self.config, self.args.checkpoint_path)
self.test_loader, self.config, self.args.checkpoint_path)
infer_model.eval()
feat_dim = self.test_loader.collate_fn.feature_size
static_model = paddle.jit.to_static(

2
deepspeech/exps/u2/model.py
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@ -506,7 +506,7 @@ class U2Tester(U2Trainer):
List[paddle.static.InputSpec]: input spec.
"""
from deepspeech.models.u2 import U2InferModel
infer_model = U2InferModel.from_pretrained(self.test_loader.dataset,
infer_model = U2InferModel.from_pretrained(self.test_loader,
self.config.model.clone(),
self.args.checkpoint_path)
feat_dim = self.test_loader.collate_fn.feature_size

95
deepspeech/frontend/augmentor/spec_augment.py
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@ -124,7 +124,7 @@ class SpecAugmentor(AugmentorBase):
def time_warp(xs, W=40):
raise NotImplementedError
def randomize_parameters(self, n_bins, n_frame):
def randomize_parameters(self, n_frame, n_bins):
# n_bins = xs.shape[0]
# n_frames = xs.shape[1]
@ -156,66 +156,69 @@ class SpecAugmentor(AugmentorBase):
self.t_0.append(int(self._rng.uniform(low=0, high=n_frames - t)))
def apply(self, xs: np.ndarray):
n_bins = xs.shape[0]
n_frames = xs.shape[1]
'''
input xs [T, D]
'''
n_frames = xs.shape[0]
n_bins = xs.shape[1]
for i in range(0, self.n_freq_masks):
f = self.f[i]
f_0 = self.f_0[i]
xs[f_0:f_0 + f, :] = 0
xs[:, f_0:f_0 + f] = 0
assert f_0 <= f_0 + f
for i in range(self.n_masks):
t = self.t[i]
t_0 = self.t_0[i]
xs[:, t_0:t_0 + t] = 0
xs[t_0:t_0 + t, :] = 0
assert t_0 <= t_0 + t
return xs
def mask_freq(self, xs, replace_with_zero=False):
n_bins = xs.shape[0]
for i in range(0, self.n_freq_masks):
f = int(self._rng.uniform(low=0, high=self.F))
f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
xs[f_0:f_0 + f, :] = 0
assert f_0 <= f_0 + f
self._freq_mask = (f_0, f_0 + f)
return xs
def mask_time(self, xs, replace_with_zero=False):
n_frames = xs.shape[1]
if self.adaptive_number_ratio > 0:
n_masks = int(n_frames * self.adaptive_number_ratio)
n_masks = min(n_masks, self.max_n_time_masks)
else:
n_masks = self.n_time_masks
if self.adaptive_size_ratio > 0:
T = self.adaptive_size_ratio * n_frames
else:
T = self.T
# def mask_freq(self, xs, replace_with_zero=False):
# n_bins = xs.shape[0]
# for i in range(0, self.n_freq_masks):
# f = int(self._rng.uniform(low=0, high=self.F))
# f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
# xs[f_0:f_0 + f, :] = 0
# assert f_0 <= f_0 + f
# self._freq_mask = (f_0, f_0 + f)
# return xs
for i in range(n_masks):
t = int(self._rng.uniform(low=0, high=T))
t = min(t, int(n_frames * self.p))
t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
xs[:, t_0:t_0 + t] = 0
assert t_0 <= t_0 + t
self._time_mask = (t_0, t_0 + t)
return xs
# def mask_time(self, xs, replace_with_zero=False):
# n_frames = xs.shape[1]
# if self.adaptive_number_ratio > 0:
# n_masks = int(n_frames * self.adaptive_number_ratio)
# n_masks = min(n_masks, self.max_n_time_masks)
# else:
# n_masks = self.n_time_masks
# if self.adaptive_size_ratio > 0:
# T = self.adaptive_size_ratio * n_frames
# else:
# T = self.T
# for i in range(n_masks):
# t = int(self._rng.uniform(low=0, high=T))
# t = min(t, int(n_frames * self.p))
# t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
# xs[:, t_0:t_0 + t] = 0
# assert t_0 <= t_0 + t
# self._time_mask = (t_0, t_0 + t)
# return xs
def transform_feature(self, xs: np.ndarray, single=True):
"""
Args:
xs (FloatTensor): `[F, T]`
Returns:
xs (FloatTensor): `[F, T]`
"""
if(single):
self.randomize_parameters(xs)
return self.apply(xs)
# def transform_feature(self, xs: np.ndarray, single=True):
# """
# Args:
# xs (FloatTensor): `[F, T]`
# Returns:
# xs (FloatTensor): `[F, T]`
# """
# if(single):
# self.randomize_parameters(xs)
# return self.apply(xs)
# def transform_feature(self, xs: np.ndarray):
# """

10
deepspeech/frontend/featurizer/audio_featurizer.py
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@ -221,17 +221,19 @@ class AudioFeaturizer(object):
"""append delat, delta-delta feature.
Args:
feat (np.ndarray): (D, T)
feat (np.ndarray): (T, D)
Returns:
np.ndarray: feat with delta-delta, (3*D, T)
np.ndarray: feat with delta-delta, (T, 3*D)
"""
# transpose (T, D) --> (D, T)
feat = np.transpose(feat)
# Deltas
d_feat = delta(feat, 2)
# Deltas-Deltas
dd_feat = delta(feat, 2)
# transpose
# transpose (D, T) --> (T, D)
feat = np.transpose(feat)
d_feat = np.transpose(d_feat)
dd_feat = np.transpose(dd_feat)
@ -264,7 +266,7 @@ class AudioFeaturizer(object):
ValueError: stride_ms > window_ms
Returns:
np.ndarray: mfcc feature, (D, T).
np.ndarray: mfcc feature, (T, D).
"""
if max_freq is None:
max_freq = sample_rate / 2
@ -322,7 +324,7 @@ class AudioFeaturizer(object):
ValueError: stride_ms > window_ms
Returns:
np.ndarray: mfcc feature, (D, T).
np.ndarray: mfcc feature, (T, D).
"""
if max_freq is None:
max_freq = sample_rate / 2

13
deepspeech/io/collator.py
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@ -229,7 +229,7 @@ class SpeechCollator():
def randomize_feature_parameters(self, n_bins, n_frames):
self._augmentation_pipeline.andomize_parameters_feature_transform(n_bins, n_frames)
def process_utterance(self, audio_file, transcript):
def process_feature_and_transform(self, audio_file, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
@ -254,6 +254,7 @@ class SpeechCollator():
# # apply specgram augment
# specgram = self._augmentation_pipeline.apply_feature_transform(specgram)
return specgram, transcript_part
@ -318,12 +319,12 @@ class SpeechCollator():
for utt, audio, text in batch:
if not self.config.randomize_each_batch:
self.randomize_audio_parameters()
audio, text = self.process_utterance(audio, text)
audio, text = self.process_feature_and_transform(audio, text)
#utt
utts.append(utt)
# audio
audios.append(audio.T) # [T, D]
audio_lens.append(audio.shape[1])
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
# text
# for training, text is token ids
# else text is string, convert to unicode ord
@ -346,8 +347,8 @@ class SpeechCollator():
text_lens = np.array(text_lens).astype(np.int64)
#spec augment
n_bins=padded_audios[0]
self.randomize_feature_parameters(n_bins, min(audio_lens))
n_bins=padded_audios.shape[2]
self.randomize_feature_parameters(min(audio_lens), n_bins)
for i in range(len(padded_audios)):
if not self.config.randomize_each_batch:
self.randomize_feature_parameters(n_bins, audio_lens[i])

8
deepspeech/models/deepspeech2.py
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@ -198,11 +198,11 @@ class DeepSpeech2Model(nn.Layer):
cutoff_top_n, num_processes)
@classmethod
def from_pretrained(cls, dataset, config, checkpoint_path):
def from_pretrained(cls, dataloader, config, checkpoint_path):
"""Build a DeepSpeech2Model model from a pretrained model.
Parameters
----------
dataset: paddle.io.Dataset
dataloader: paddle.io.DataLoader
config: yacs.config.CfgNode
model configs
@ -215,8 +215,8 @@ class DeepSpeech2Model(nn.Layer):
DeepSpeech2Model
The model built from pretrained result.
"""
model = cls(feat_size=dataset.feature_size,
dict_size=dataset.vocab_size,
model = cls(feat_size=dataloader.collate_fn.feature_size,
dict_size=dataloader.collate_fn.vocab_size,
num_conv_layers=config.model.num_conv_layers,
num_rnn_layers=config.model.num_rnn_layers,
rnn_size=config.model.rnn_layer_size,

8
deepspeech/models/u2.py
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@ -876,11 +876,11 @@ class U2Model(U2BaseModel):
return model
@classmethod
def from_pretrained(cls, dataset, config, checkpoint_path):
def from_pretrained(cls, dataloader, config, checkpoint_path):
"""Build a DeepSpeech2Model model from a pretrained model.
Args:
dataset (paddle.io.Dataset): not used.
dataloader (paddle.io.DataLoader): not used.
config (yacs.config.CfgNode): model configs
checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name
@ -888,8 +888,8 @@ class U2Model(U2BaseModel):
DeepSpeech2Model: The model built from pretrained result.
"""
config.defrost()
config.input_dim = dataset.feature_size
config.output_dim = dataset.vocab_size
config.input_dim = dataloader.collate_fn.feature_size
config.output_dim = dataloader.collate_fn.vocab_size
config.freeze()
model = cls.from_config(config)

4
deepspeech/utils/socket_server.py
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@ -48,9 +48,9 @@ def warm_up_test(audio_process_handler,
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'])
print("Warm-up Test Case %d: %s" % (idx, sample['feat']))
start_time = time.time()
transcript = audio_process_handler(sample['audio_filepath'])
transcript = audio_process_handler(sample['feat'])
finish_time = time.time()
print("Response Time: %f, Transcript: %s" %
(finish_time - start_time, transcript))

13
examples/aishell/s0/README.md
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@ -2,9 +2,10 @@
## Deepspeech2
| Model | release | Config | Test set | Loss | CER |
| --- | --- | --- | --- | --- | --- |
| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |
| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |
| DeepSpeech2 | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 |
| DeepSpeech2 | 1.8.5 | - | test | - | 0.080447 |
| Model | Params | Release | Config | Test set | Loss | CER |
| --- | --- | --- | --- | --- | --- | --- |
| DeepSpeech2 | 58.4M | 2.2.0 | conf/deepspeech2.yaml + spec aug + new datapipe | test | 6.396368026733398 | 0.068382 |
| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |
| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |
| DeepSpeech2 | 58.4M | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 |
| DeepSpeech2 | 58.4M | 1.8.5 | - | test | - | 0.080447 |

3
examples/aishell/s0/conf/deepspeech2.yaml
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@ -10,8 +10,8 @@ data:
min_output_input_ratio: 0.00
max_output_input_ratio: .inf
collator:
batch_size: 64 # one gpu
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/vocab.txt
@ -33,7 +33,6 @@ collator:
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 0
batch_size: 64 # one gpu
model:
num_conv_layers: 2

6
examples/aishell/s0/run.sh
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@ -2,7 +2,7 @@
set -e
source path.sh
gpus=0
gpus=0,1,2,3
stage=0
stop_stage=100
conf_path=conf/deepspeech2.yaml
@ -31,10 +31,10 @@ fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi

24
examples/aishell/s1/README.md
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@ -2,21 +2,21 @@
## Conformer
| Model | Config | Augmentation| Test set | Decode method | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- |
| conformer | conf/conformer.yaml | spec_aug + shift | test | attention | - | 0.059858 |
| conformer | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | - | 0.062311 |
| conformer | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | - | 0.062196 |
| conformer | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | - | 0.054694 |
| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | attention | - | 0.059858 |
| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | - | 0.062311 |
| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | - | 0.062196 |
| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | - | 0.054694 |
## Chunk Conformer
| Model | Config | Augmentation| Test set | Decode method | Chunk | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16 | - | 0.061939 |
| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16 | - | 0.070806 |
| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16 | - | 0.070739 |
| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16 | - | 0.059400 |
| Model | Params | Config | Augmentation| Test set | Decode method | Chunk | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16 | - | 0.061939 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16 | - | 0.070806 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16 | - | 0.070739 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16 | - | 0.059400 |
## Transformer

10
examples/librispeech/s0/README.md
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@ -2,8 +2,8 @@
## Deepspeech2
| Model | release | Config | Test set | Loss | WER |
| --- | --- | --- | --- | --- | --- |
| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml | 15.184467315673828 | test-clean | 0.072154 |
| DeepSpeech2 | 2.0.0 | conf/deepspeech2.yaml | - | test-clean | 0.073973 |
| DeepSpeech2 | 1.8.5 | - | test-clean | - | 0.074939 |
| Model | Params | Release | Config | Test set | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- |
| DeepSpeech2 | 42.96M | 2.1.0 | conf/deepspeech2.yaml | 15.184467315673828 | test-clean | 0.072154 |
| DeepSpeech2 | 42.96M | 2.0.0 | conf/deepspeech2.yaml | - | test-clean | 0.073973 |
| DeepSpeech2 | 42.96M | 1.8.5 | - | test-clean | - | 0.074939 |

13
examples/librispeech/s0/conf/deepspeech2.yaml
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@ -3,16 +3,21 @@ data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev-clean
test_manifest: data/manifest.test-clean
mean_std_filepath: data/mean_std.json
vocab_filepath: data/vocab.txt
augmentation_config: conf/augmentation.json
batch_size: 20
min_input_len: 0.0
max_input_len: 27.0 # second
min_output_len: 0.0
max_output_len: .inf
min_output_input_ratio: 0.00
max_output_input_ratio: .inf
collator:
batch_size: 20
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
specgram_type: linear
target_sample_rate: 16000
max_freq: None

4
tools/Makefile
Unescape View File

@ -31,5 +31,5 @@ sox.done:
soxbindings.done:
test -d soxbindings || git clone https://github.com/pseeth/soxbindings.git
source venv/bin/activate; cd soxbindings && python3 setup.py install
touch soxbindings.done
source venv/bin/activate; cd soxbindings && python setup.py install
touch soxbindings.done
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