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STT Zh Quartznet15x5

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Description

QuartzNet is a Jasper-like network that uses separable convolutions and larger filter sizes. It has comparable accuracy to Jasper while having much fewer parameters. This particular model has 15 blocks each repeated 5 times.

Publisher

NVIDIA

Use Case

Other

Framework

PyTorch with NeMo

Latest Version

1.0.0rc1

Modified

June 30, 2021

Size

87.51 MB

Model Overview

QuartzNet15x5 checkpoints available here are trained using NeMo on the Ai-shell 2 Mandarin Chinese dataset.

It utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the Aishell-2 Mandard Corpus.

Model Architecture

The Quartznet model is composed of multiple blocks with residual connections between them, trained with CTC loss. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers.

The Quartznet 15x5 model consists of 79 layers and has a total of 18.9 million parameters, with five blocks that repeat fifteen times plus four additional convolutional layers [1].

Training

This model was trained on the open source Aishell-2 [2] corpus consisting of about 1000 hours transcribed Mandarin speech. The NeMo toolkit [3] was used for training this model over several hundred epochs on multiple GPUs.

The model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it more robust to noise and is also augmented with narrowband speech codecs to be more accurate with 8 kHz telephony data.

Datasets

While training this model, we used the following datasets:

  • Aishell-2 corpus

Performance

The performance of Automatic Speech Recognition models is measuring using Character Error Rate.

The model obtains the following scores on the following evaluation datasets -

  • 7.2 % on Aishell-2 dev_ios
  • 7.2 % on Aishell-2 test_ios

Note that these scores on Aishell-2 are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.

How to Use this Model

The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_zh_quartznet15x5")

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
  pretrained_name="stt_zh_quartznet15x5" \
  audio_dir=""

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

References

[1] QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions

[2] AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.