---
language:
  - fr
library_name: nemo
datasets:
  - multilingual_librispeech
  - mozilla-foundation/common_voice_7_0
  - VoxPopuli
thumbnail: null
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
license: cc-by-4.0
model-index:
  - name: stt_fr_conformer_transducer_large
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MCV 7.0
          type: mozilla-foundation/common_voice_7_0
          config: fr
          split: dev
          args:
            language: fr
        metrics:
          - name: Dev WER
            type: wer
            value: 6.85
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MCV 7.0
          type: mozilla-foundation/common_voice_7_0
          config: fr
          split: test
          args:
            language: fr
        metrics:
          - name: Test WER
            type: wer
            value: 7.95
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Multilingual Librispeech
          type: multilingual_librispeech
          config: fr
          split: dev
          args:
            language: fr
        metrics:
          - name: Dev WER
            type: wer
            value: 5.05
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Multilingual Librispeech
          type: multilingual_librispeech
          config: fr
          split: test
          args:
            language: fr
        metrics:
          - name: Test WER
            type: wer
            value: 4.1
---

# NVIDIA Conformer-Transducer Large (fr) (FORK)

<style>
img {
 display: inline;
}
</style>

| [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--Transducer-lightgrey#model-badge)](#model-architecture)
| [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture)
| [![Language](https://img.shields.io/badge/Language-fr-lightgrey#model-badge)](#datasets)

This model was trained on a composite dataset comprising of over 1500 hours of French speech. It is a large size version of Conformer-Transducer (around 120M parameters).
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-transducer) for complete architecture details.

## NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/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

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_fr_conformer_transducer_large")
```

### Transcribing using Python

First, let's get a sample

```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```

Then simply do:

```
asr_model.transcribe(['2086-149220-0033.wav'])
```

### Transcribing many audio files

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

### 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.

## Model Architecture

Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).

## Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).

The sentence-piece tokenizers [2] for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).

## Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of over a thousand hours of French speech:

- MozillaCommonVoice 7.0 - 356 hours
- Multilingual LibriSpeech - 1036 hours
- VoxPopuli - 182 hours

Both models use same dataset, excluding a preprocessing step to strip hyphen from data for secondary model's training.

## 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 latest model obtains the following greedy scores on the following evaluation datasets

- 6.85 % on MCV7.0 dev
- 7.95 % on MCV7.0 test
- 5.05 % on MLS dev
- 4.10 % on MLS test

Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of hyphenation and apostrophe.

## Limitations

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. The model might also perform worse for accented speech.

Further, since portions of the training set contain text from both pre- and post- 1990 orthographic reform, regularity of punctuation may vary between the two styles.
For downstream tasks requiring more consistency, finetuning or downstream processing may be required. If exact orthography is not necessary, then using secondary model is advised.

## References

- [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)

- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)