metadata

language:
  - uz
library_name: nemo
datasets:
  - mozilla-foundation/common-voice-17-0
  - UzbekVoice
  - google/fleurs
thumbnail: null
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - FastConformer
  - CTC
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
license: cc-by-4.0
model-index:
  - name: stt_uz_fastconformer_hybrid_large_pc
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common-voice-17-0
          type: mozilla-foundation/common_voice_17_0
          config: uz
          split: test
          args:
            language: uz
        metrics:
          - name: Test WER
            type: wer
            value: 16.46
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: uz
          split: dev
          args:
            language: uz
        metrics:
          - name: Test WER
            type: wer
            value: 17.52

NVIDIA FastConformer-Hybrid Large (uz)

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This model transcribes text in upper and lower case Uzbek alphabet with spaces, commas, question marks, and dashes.
It is a "large" version of FastConformer Transducer-CTC (around 115M parameters) model. This is a hybrid model trained on two losses: Transducer (default) and CTC. See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest Pytorch version.

pip install nemo_toolkit['all']

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically instantiate the model

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="nvidia/stt_uz_fastconformer_hybrid_large_pc")

Transcribing using Python

Simply do:

asr_model.transcribe(['audio_file.wav'])

Transcribing many audio files

Using Transducer mode inference:

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
 pretrained_name="nvidia/stt_uz_fastconformer_hybrid_large_pc" 
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

Using CTC mode inference:

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
 pretrained_name="nvidia/stt_uz_fastconformer_hybrid_large_pc" 
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
 decoder_type="ctc"

Input

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

Output

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

Model Architecture

FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with a Transducer decoder loss. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

The model is trained on three composite datasets comprising of 1000 hours of Uzbek speech:

MCV 17.0 Uzbek (~90 hrs)
UzbekVoice (~900 hrs)
Fleurs Uzbek (~10 hrs)

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The following tables summarizes the performance of the model with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

	WER(%)	WER wo CAP	WER wo PUNCT	WER wo PUNCT CAP
FLEURS DEV (used as test)	17.52	16.20	12.20	10.73
MCV TEST	16.46	15.89	7.78	7.18

Limitations

The model is non-streaming and outputs the speech as a string without capitalization and punctuation. Since this model was trained on publicly 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.

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:

World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

Although this model isn’t supported yet by Riva, the list of supported models is here.
Check out Riva live demo.

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[2] Google Sentencepiece Tokenizer

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.