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	---
	license: apache-2.0
	language: fr
	library_name: nemo
	datasets:
	- mozilla-foundation/common_voice_13_0
	- multilingual_librispeech
	- facebook/voxpopuli
	- google/fleurs
	- gigant/african_accented_french
	thumbnail: null
	tags:
	- automatic-speech-recognition
	- speech
	- audio
	- Transducer
	- FastConformer
	- CTC
	- Transformer
	- pytorch
	- NeMo
	- hf-asr-leaderboard
	model-index:
	- name: stt_fr_fastconformer_hybrid_large
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 13.0
	type: mozilla-foundation/common_voice_13_0
	config: fr
	split: test
	args:
	language: fr
	metrics:
	- name: WER
	type: wer
	value: 9.16
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: Multilingual LibriSpeech (MLS)
	type: facebook/multilingual_librispeech
	config: french
	split: test
	args:
	language: fr
	metrics:
	- name: WER
	type: wer
	value: 4.82
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: VoxPopuli
	type: facebook/voxpopuli
	config: french
	split: test
	args:
	language: fr
	metrics:
	- name: WER
	type: wer
	value: 9.23
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: Fleurs
	type: google/fleurs
	config: fr_fr
	split: test
	args:
	language: fr
	metrics:
	- name: WER
	type: wer
	value: 8.65
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: African Accented French
	type: gigant/african_accented_french
	config: fr
	split: test
	args:
	language: fr
	metrics:
	- name: WER
	type: wer
	value: 6.55
	---

	# FastConformer-Hybrid Large (fr)

	<style>
	img {
	display: inline;
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	\| [![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--Transducer_CTC-lightgrey#model-badge)](#model-architecture)
	\| [![Model size](https://img.shields.io/badge/Params-115M-lightgrey#model-badge)](#model-architecture)
	\| [![Language](https://img.shields.io/badge/Language-fr-lightgrey#model-badge)](#datasets)

	This model aims to replicate [nvidia/stt_fr_fastconformer_hybrid_large_pc](https://huggingface.co/nvidia/stt_fr_fastconformer_hybrid_large_pc) with the goal of predicting only the lowercase French alphabet, hyphen, and apostrophe. While this choice sacrifices broader functionalities like predicting casing, numbers, and punctuation, it can enhance accuracy for specific use cases.

	Similar to its sibling, this is a "large" version of the FastConformer Transducer-CTC model (around 115M parameters). It's a hybrid model trained using two loss functions: Transducer (default) and CTC.

	## Performance

	We evaluated our model on the following datasets and re-ran the evaluation on other models for comparison. Please note that the reported WER is the result after converting numbers to text, removing punctuation (except for apostrophes and hyphens), and converting all characters to lowercase.

	![Benchmarks](https://huggingface.co/bofenghuang/stt_fr_fastconformer_hybrid_large/resolve/main/assets/bench.png)

	All the evaluation results can be found [here](https://drive.google.com/drive/folders/1adZTgGAptYx2ut9jddjmlj5--dkY2XWZ?usp=sharing).

	## Usage

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

	```python
	# Install nemo
	# !pip install nemo_toolkit['all']

	import nemo.collections.asr as nemo_asr

	model_name = "bofenghuang/stt_fr_fastconformer_hybrid_large"
	asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=model_name)

	# Path to your 16kHz mono-channel audio file
	audio_path = "/path/to/your/audio/file"

	# Transcribe with defaut transducer decoder
	asr_model.transcribe([audio_path])

	# (Optional) Switch to CTC decoder
	asr_model.change_decoding_strategy(decoder_type="ctc")

	# (Optional) Transcribe with CTC decoder
	asr_model.transcribe([audio_path])
	```

	## Datasets

	This model has been trained on a composite dataset comprising over 2500 hours of French speech audio and transcriptions, including [Common Voice 13.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Fleurs](https://huggingface.co/datasets/google/fleurs), [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french), and more.

	## 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.

	The model exclusively generates the lowercase French alphabet, hyphen, and apostrophe. Therefore, it may not perform well in situations where uppercase characters and additional punctuation are also required.

	## References

	[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)

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

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

	## Acknowledgements

	Thanks to Nvidia's research on the advanced model architecture and the NeMo team's training framework.