espnet_onnx

ESPnet without PyTorch!

Utility library to easily export, quantize, and optimize espnet models to onnx format. There is no need to install PyTorch or ESPnet on your machine if you already have exported files!

espnet_onnx demo in Colab

Now demonstration notebook is available in google colab!

• Simple ASR demo:
• Simple TTS demo:

Install

1. espnet_onnx can be installed with pip

pip install espnet_onnx

2. If you want to export pretrained model, you need to install torch>=1.11.0, espnet, espnet_model_zoo, onnx additionally. onnx==1.12.0 might cause some errors. If you got an error while inference or exporting, please consider downgrading the onnx version.

Install guide for developers

1. Clone this repository.

git clone [email protected]:espnet/espnet_onnx.git

2. Create virtual environment.

cd tools
make venv export

3. Activate virtual environment and install torch if required.

. tools/venv/bin/activate

# Please reference official installation guide of PyTorch.
pip install torch

4. Clone the s3prl repository and install with pip.

cd tools
git clone https://github.com/s3prl/s3prl
cd s3prl
pip install .

5. Install warp_transducer for developing transducer model.

cd tools
git clone --single-branch --branch espnet_v1.1 https://github.com/b-flo/warp-transducer.git
cd warp-transducer
mkdir build
# Please set WITH_OMP to ON or OFF.
cd build && cmake -DWITH_OMP="ON" .. && make
cd pytorch_binding && python3 -m pip install -e .

6. If you want to develop optimization, you also need to develop onnxruntime. Please clone the onnxruntime repository.

7. Since espnet==202308(latest on v0.2.0 release) requires protobuf<=3.20.1 while the latest onnx requires protobuf>=3.20.2, you may get error with installation. In this case, first, install the espnet==202308, update protobuf to 3.20.3, and then install other libraries.

Usage

Export models

1. espnet_onnx can export pretrained model published on espnet_model_zoo. By default, exported files will be stored in ${HOME}/.cache/espnet_onnx/<tag_name>.

from espnet2.bin.asr_inference import Speech2Text
from espnet_onnx.export import ASRModelExport

m = ASRModelExport()

# download with espnet_model_zoo and export from pretrained model
m.export_from_pretrained('<tag name>', quantize=True)

# export from trained model
speech2text = Speech2Text(args)
m.export(speech2text, '<tag name>', quantize=True)

2. You can export pretrained model from zipped file. The zipped file should contain meta.yaml.

from espnet_onnx.export import ASRModelExport

m = ASRModelExport()
m.export_from_zip(
  'path/to/the/zipfile',
  tag_name='tag_name_for_zipped_model',
  quantize=True
)

3. You can set some configuration for export. The available configurations are shown in the details for each models.

• Details for ASR models and configurations
• Details for TTS models and configurations
• Details for optimization configurations

from espnet_onnx.export import ASRModelExport

m = ASRModelExport()
# Set maximum sequence length to 3000
m.set_export_config(max_seq_len=3000)
m.export_from_zip(
  'path/to/the/zipfile',
  tag_name='tag_name_for_zipped_model',
)

1. You can easily optimize your model by using the optimize option. If you want to fully optimize your model, you need to install the custom version of onnxruntime from here. Please read this document for more detail.

from espnet_onnx.export import ASRModelExport

m = ASRModelExport()
m.export_from_zip(
  'path/to/the/zipfile',
  tag_name='tag_name_for_zipped_model',
  optimize=True,
  quantize=True
)

5. You can export model from the command line.

python -m espnet_onnx.export \
  --model_type asr \
  --input ${path_to_zip} \
  --tag transformer_lm \
  --apply_optimize \
  --apply_quantize

Inference

1. For inference, tag_name or model_dir is used to load onnx file. tag_name has to be defined in tag_config.yaml

import librosa
from espnet_onnx import Speech2Text

speech2text = Speech2Text(tag_name='<tag name>')
# speech2text = Speech2Text(model_dir='path to the onnx directory')

y, sr = librosa.load('sample.wav', sr=16000)
nbest = speech2text(y)

3. For streaming asr, you can use StreamingSpeech2Text class. The speech length should be the same as StreamingSpeech2Text.hop_size

from espnet_onnx import StreamingSpeech2Text

stream_asr = StreamingSpeech2Text(tag_name)

# start streaming asr
stream_asr.start()
while streaming:
  wav = <some code to get wav>
  assert len(wav) == stream_asr.hop_size
  stream_text = stream_asr(wav)[0][0]

# You can get non-streaming asr result with end function
nbest = stream_asr.end()

You can also simulate streaming model with your wav file with simulate function. Passing True as the second argument will show the streaming text as the following code.

import librosa
from espnet_onnx import StreamingSpeech2Text

stream_asr = StreamingSpeech2Text(tag_name)
y, sr = librosa.load('path/to/wav', sr=16000)
nbest = stream_asr.simulate(y, True)
# Processing audio with 6 processes.
# Result at position 0 :
# Result at position 1 :
# Result at position 2 : this
# Result at position 3 : this is
# Result at position 4 : this is a
# Result at position 5 : this is a
print(nbest[0][0])
# 'this is a pen'

4. If you installed the custom version of onnxruntime, you can run optimized model for inference. You don't have to change any code from the above. If the model was optimized, then espnet_onnx would automatically load the optimized version.

5. You can use only hubert model for your frontend.

from espnet_onnx.export import ASRModelExport

# export your model
tag_name = 'ESPnet pretrained model with hubert'
m = ASRModelExport()
m.export_from_pretrained(tag_name, optimize=True)

# load only the frontend model
from espnet_onnx.asr.frontend import Frontend
frontend = Frontend.get_frontend(tag_name)

# use the model in your application
import librosa
y, sr = librosa.load('wav file')
# y: (B, T)
# y_len: (B,)
feats = frontend(y[None,:], np.array([len(y)]))

6. If you installed torch in your environment, you can use frontend in your training.

from espnet_onnx.asr.frontend import TorchFrontend
frontend = TorchFrontend.get_frontend(tag_name) # load pretrained frontend model

# use the model while training
import librosa
y, sr = librosa.load('wav file')

# You need to place your data on GPU,
# and specify the output shape in tuple
y = torch.Tensor(y).unsqueeze(0).to('cuda') # (1, wav_length)
output_shape = (batch_size, feat_length, feats_dims)
feats = frontend(y, y.size(1), output_shape)

Text2Speech inference

1. You can export TTS models as ASR models.

from espnet2.bin.tts_inference import Text2Speech
from espnet_onnx.export import TTSModelExport

m = TTSModelExport()

# download with espnet_model_zoo and export from pretrained model
m.export_from_pretrained('<tag name>', quantize=True)

# export from trained model
text2speech = Text2Speech(args)
m.export(text2speech, '<tag name>', quantize=True)

2. You can generate wav files with just simply using the Text2Speech class.

from espnet_onnx import Text2Speech

tag_name = 'kan-bayashi/ljspeech_vits'
text2speech = Text2Speech(tag_name, use_quantized=True)

text = 'Hello world!'
output_dict = text2speech(text) # inference with onnx model.
wav = output_dict['wav']

How to use GPU on espnet_onnx

Install dependency.

First, we need onnxruntime-gpu library, instead of onnxruntime. Please follow this article to select and install the correct version of onnxruntime-gpu, depending on your CUDA version.

Inference on GPU

Now you can speedup the inference speed with GPU. All you need is to select the correct providers, and give it to the Speech2Text or StreamingSpeech2Text instance. See this article for more information about providers.

import librosa
from espnet_onnx import Speech2Text

PROVIDERS = ['CUDAExecutionProvider']
tag_name = 'some_tag_name'

speech2text = Speech2Text(
  tag_name,
  providers=PROVIDERS
)
y, sr = librosa.load('path/to/wav', sr=16000)
nbest = speech2text(y) # runs on GPU.

Note that some quantized models are not supported for GPU computation. If you got an error with quantized model, please try not-quantized model.

Changes from ESPNet

To avoid the cache problem, I modified some scripts from the original espnet implementation.

1. Add <blank> before <sos>

2. Give some torch.zeros() arrays to the model.

3. Remove the first token in post process. (remove blank)

4. Replace make_pad_mask into new implementation, which can be converted into onnx format.

5. Removed extend_pe() from positional encoding module. The length of pe is 512 by default.

Supported Archs

ASR: Supported architecture for ASR

TTS: Supported architecture for TTS

Developer's Guide

ASR: Developer's Guide

References

• ESPNet: end-to-end speech processing toolkit
• ESPNet Model Zoo

COPYRIGHT

Copyright (c) 2022 Maso Someki

Released under MIT licence

Author

Masao Someki

contact: [email protected]