wav2vec2-large-xlsr-53-dutch / README.md

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	---
	language: nl
	license: apache-2.0
	datasets:
	- common_voice
	- mozilla-foundation/common_voice_6_0
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- hf-asr-leaderboard
	- mozilla-foundation/common_voice_6_0
	- nl
	- robust-speech-event
	- speech
	- xlsr-fine-tuning-week
	model-index:
	- name: XLSR Wav2Vec2 Dutch by Jonatas Grosman
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice nl
	type: common_voice
	args: nl
	metrics:
	- name: Test WER
	type: wer
	value: 15.72
	- name: Test CER
	type: cer
	value: 5.35
	- name: Test WER (+LM)
	type: wer
	value: 12.84
	- name: Test CER (+LM)
	type: cer
	value: 4.64
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: nl
	metrics:
	- name: Dev WER
	type: wer
	value: 35.79
	- name: Dev CER
	type: cer
	value: 17.67
	- name: Dev WER (+LM)
	type: wer
	value: 31.54
	- name: Dev CER (+LM)
	type: cer
	value: 16.37
	---

	# Fine-tuned XLSR-53 large model for speech recognition in Dutch

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
	When using this model, make sure that your speech input is sampled at 16kHz.

	This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)

	The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

	## Usage

	The model can be used directly (without a language model) as follows...

	Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:

	```python
	from huggingsound import SpeechRecognitionModel

	model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-dutch")
	audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

	transcriptions = model.transcribe(audio_paths)
	```

	Writing your own inference script:

	```python
	import torch
	import librosa
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	LANG_ID = "nl"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-dutch"
	SAMPLES = 10

	test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = batch["sentence"].upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

	predicted_ids = torch.argmax(logits, dim=-1)
	predicted_sentences = processor.batch_decode(predicted_ids)

	for i, predicted_sentence in enumerate(predicted_sentences):
	print("-" * 100)
	print("Reference:", test_dataset[i]["sentence"])
	print("Prediction:", predicted_sentence)
	```

	\| Reference \| Prediction \|
	\| ------------- \| ------------- \|
	\| DE ABORIGINALS ZIJN DE OORSPRONKELIJKE BEWONERS VAN AUSTRALIË. \| DE ABBORIGENALS ZIJN DE OORSPRONKELIJKE BEWONERS VAN AUSTRALIË \|
	\| MIJN TOETSENBORD ZIT VOL STOF. \| MIJN TOETSENBORD ZIT VOL STOF \|
	\| ZE HAD DE BANK BESCHADIGD MET HAAR SKATEBOARD. \| ZE HAD DE BANK BESCHADIGD MET HAAR SCHEETBOORD \|
	\| WAAR LAAT JIJ JE ONDERHOUD DOEN? \| WAAR LAAT JIJ HET ONDERHOUD DOEN \|
	\| NA HET LEZEN VAN VELE BEOORDELINGEN HAD ZE EINDELIJK HAAR OOG LATEN VALLEN OP EEN LAPTOP MET EEN QWERTY TOETSENBORD. \| NA HET LEZEN VAN VELE BEOORDELINGEN HAD ZE EINDELIJK HAAR OOG LATEN VALLEN OP EEN LAPTOP MET EEN QUERTITOETSEMBORD \|
	\| DE TAMPONS ZIJN OP. \| DE TAPONT ZIJN OP \|
	\| MARIJKE KENT OLIVIER NU AL MEER DAN TWEE JAAR. \| MAARRIJKEN KENT OLIEVIER NU AL MEER DAN TWEE JAAR \|
	\| HET VOEREN VAN BROOD AAN EENDEN IS EIGENLIJK ONGEZOND VOOR DE BEESTEN. \| HET VOEREN VAN BEUROT AAN EINDEN IS EIGENLIJK ONGEZOND VOOR DE BEESTEN \|
	\| PARKET MOET JE STOFZUIGEN, TEGELS MOET JE DWEILEN. \| PARKET MOET JE STOF ZUIGEN MAAR TEGELS MOET JE DWEILEN \|
	\| IN ONZE BUURT KENT IEDEREEN ELKAAR. \| IN ONZE BUURT KENT IEDEREEN ELKAAR \|

	## Evaluation

	1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`

	```bash
	python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-dutch --dataset mozilla-foundation/common_voice_6_0 --config nl --split test
	```

	2. To evaluate on `speech-recognition-community-v2/dev_data`

	```bash
	python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-dutch --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
	```

	## Citation
	If you want to cite this model you can use this:

	```bibtex
	@misc{grosman2021xlsr53-large-dutch,
	title={Fine-tuned {XLSR}-53 large model for speech recognition in {D}utch},
	author={Grosman, Jonatas},
	howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-dutch}},
	year={2021}
	}
	```