README.md

metadata

language:
  - bm
library_name: nemo
datasets:
  - RobotsMali/kunkado
thumbnail: null
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - FastConformer
  - Conformer
  - pytorch
  - Bambara
  - NeMo
license: cc-by-4.0
base_model: RobotsMali/soloba-ctc-0.6b-v0
model-index:
  - name: soloba-ctc-0.6b-v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Kunkado
          type: RobotsMali/kunkado
          split: test
          args:
            language: bm
        metrics:
          - name: Test WER
            type: wer
            value: 42.80104712041885
          - name: Test CER
            type: cer
            value: 24.927870915653497
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Nyana Eval
          type: RobotsMali/nyana-eval
          split: test
          args:
            language: bm
        metrics:
          - name: Test WER
            type: wer
            value: 40.19
          - name: Test CER
            type: cer
            value: 20.94
metrics:
  - wer
  - cer
pipeline_tag: automatic-speech-recognition

Soloba-CTC-600M Series

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soloba-ctc-0.6b-v1 is a fine tuned version of RobotsMali/soloba-ctc-0.6b-v0 on RobotsMali/kunkado. This model does not consistently produce Capitalizations and Punctuations and it cannot produce acoustic event tags like those found in Kunkado its transcriptions. It was fine-tuned using NVIDIA NeMo.

🚨 Important Note

This model, along with its associated resources, is part of an ongoing research effort, improvements and refinements are expected in future versions. A human evaluation report of the model is coming soon. Users should be aware that:

• The model may not generalize very well accross all speaking conditions and dialects.
• Community feedback is welcome, and contributions are encouraged to refine the model further.

NVIDIA NeMo: Training

To 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['asr']

How to Use This Model

Note that this model has been released for research purposes primarily.

Load Model with NeMo

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="RobotsMali/soloba-ctc-0.6b-v1")

Transcribe Audio

model.eval()
# Assuming you have a test audio file named sample_audio.wav
asr_model.transcribe(['sample_audio.wav'])

Input

This model accepts any mono-channel audio (wav files) as input and resamples them to 16 kHz sample rate before performing the forward pass

Output

This model provides transcribed speech as an hypothesis object with a text attribute containing the transcription string for a given speech sample. (nemo>=2.3)

Model Architecture

This model uses a FastConformer Ecoder and a Convolutional decoder with CTC Loss. FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit was used for finetuning this model for 162,445 steps over RobotsMali/soloba-ctc-0.6b-v0 model. The finetuning codes and configurations can be found at RobotsMali-AI/bambara-asr.

The tokenizer for this model was trained on the text transcripts of the train set of RobotsMali/kunkado using this script.

Dataset

This model was fine-tuned on the kunkado dataset, the human-reviewed subset, which consists of ~40 hours of transcribed Bambara speech data. The text was normalized with the bambara-normalizer prior to training, normalizing numbers, removing punctuations and removings tags.

Performance

We report the Word Error Rate (WER) and Character Error Rate (CER) for this model:

Benchmark	Decoding	WER (%) ↓	CER (%) ↓
Kunkado	CTC	42.80	24.92
Nyana Eval	CTC	40.19	20.94

License

This model is released under the CC-BY-4.0 license. By using this model, you agree to the terms of the license.

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Feel free to open a discussion on Hugging Face or file an issue on GitHub for help or contributions.