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	---
	language:
	- fr
	thumbnail: null
	pipeline_tag: token-classification
	tags:
	- CTC
	- pytorch
	- speechbrain
	- hf-slu-leaderboard
	license: apache-2.0
	datasets:
	- MEDIA
	metrics:
	- cver
	- cer
	- cher
	model-index:
	- name: slu-wav2vec2-ctc-MEDIA-full
	results:
	- task:
	name: Spoken Language Understanding
	type: spoken-language-understanding
	dataset:
	name: MEDIA
	type: MEDIA_slu_full
	config: fr
	split: test
	args:
	language: fr
	metrics:
	- name: Test ChER
	type: cher
	value: 7.78
	- name: Test CER
	type: cer
	value: 24.88
	- name: Test CVER
	type: cver
	value: 35.77
	---

	<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
	<br/><br/>

	# wav2vec 2.0 with CTC trained on MEDIA

	This repository provides all the necessary tools to perform spoken language understanding
	from an end-to-end system pretrained on MEDIA (French Language) within
	SpeechBrain.
	Its original SpeechBrain recipe follows the paper of
	G. Laperrière, V. Pelloin, A. Caubriere, S. Mdhaffar,
	N. Camelin, S. Ghannay, B. Jabaian, Y. Estève,
	[The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools](https://aclanthology.org/2022.lrec-1.171).
	Find more about the MEDIA corpus and semantic concepts within the
	[Media ASR (ELRA-S0272)](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/) and
	[Media SLU (ELRA-E0024)](https://catalogue.elra.info/en-us/repository/browse/ELRA-E0024/) resources.
	For a better experience, we encourage you to learn more about
	[SpeechBrain](https://speechbrain.github.io).

	The performance of the model is the following:

	\| Release \| Test ChER \| Test CER \| Test CVER \| GPUs \|
	\|:-------------:\|:--------------:\|:--------------:\|:--------------:\|:--------:\|
	\| 22-02-23 \| 7.78 \| 24.88 \| 35.77 \| 1xV100 32GB \|

	## Pipeline description

	This SLU system is composed of an acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([LeBenchmark/wav2vec2-FR-3K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large)) is combined with three DNN layers and finetuned on MEDIA.
	The obtained final acoustic representation is given to the CTC greedy decoder.

	The system is trained with recordings sampled at 16kHz (single channel).
	The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed.

	## Install SpeechBrain

	First of all, please install tranformers and SpeechBrain with the following command:

	```
	pip install speechbrain transformers
	```

	Please notice that we encourage you to read our tutorials and learn more about
	[SpeechBrain](https://speechbrain.github.io).

	### Transcribing and semantically annotating your own audio files (in French)

	```python
	from speechbrain.inference.ASR import EncoderASR

	asr_model = EncoderASR.from_hparams(source="speechbrain/slu-wav2vec2-ctc-MEDIA-full", savedir="pretrained_models/slu-wav2vec2-ctc-MEDIA-full")
	asr_model.transcribe_file('speechbrain/slu-wav2vec2-ctc-MEDIA-full/example-fr.wav')

	```
	### Inference on GPU
	To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.

	### Training
	The model was trained with SpeechBrain.
	To train it from scratch follow these steps:
	1. Clone SpeechBrain:
	```bash
	git clone https://github.com/speechbrain/speechbrain/
	```
	2. Install it:
	```bash
	cd speechbrain
	pip install -r requirements.txt
	pip install -e .
	```
	3. Download MEDIA related files:
	- [Media ASR (ELRA-S0272)](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/)
	- [Media SLU (ELRA-E0024)](https://catalogue.elra.info/en-us/repository/browse/ELRA-E0024/)
	- [channels.csv and concepts_full_relax.csv](https://drive.google.com/drive/u/1/folders/1z2zFZp3c0NYLFaUhhghhBakGcFdXVRyf)
	4. Modify placeholders in hparams/train_hf_wav2vec_full.yaml:
	```bash
	data_folder = !PLACEHOLDER
	channels_path = !PLACEHOLDER
	concepts_path = !PLACEHOLDER
	```
	5. Run Training:
	```bash
	cd recipes/MEDIA/SLU/CTC/
	python train_hf_wav2vec.py hparams/train_hf_wav2vec_full.yaml
	```

	You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1cZRRQ3Cc_ucXXlwYSJqB7pOl6KwZdo84?usp=share_link).

	### Limitations
	The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

	#### Referencing SpeechBrain

	```
	@misc{SB2021,
	author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
	title = {SpeechBrain},
	year = {2021},
	publisher = {GitHub},
	journal = {GitHub repository},
	howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
	}
	```

	#### About SpeechBrain
	SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

	Website: https://speechbrain.github.io/

	GitHub: https://github.com/speechbrain/speechbrain