README.md · jonatasgrosman/wav2vec2-large-xlsr-53-german at main

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	---
	language: de
	license: apache-2.0
	datasets:
	- common_voice
	- mozilla-foundation/common_voice_6_0
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- de
	- hf-asr-leaderboard
	- mozilla-foundation/common_voice_6_0
	- robust-speech-event
	- speech
	- xlsr-fine-tuning-week
	model-index:
	- name: XLSR Wav2Vec2 German by Jonatas Grosman
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice de
	type: common_voice
	args: de
	metrics:
	- name: Test WER
	type: wer
	value: 12.06
	- name: Test CER
	type: cer
	value: 2.92
	- name: Test WER (+LM)
	type: wer
	value: 8.74
	- name: Test CER (+LM)
	type: cer
	value: 2.28
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: de
	metrics:
	- name: Dev WER
	type: wer
	value: 32.75
	- name: Dev CER
	type: cer
	value: 13.64
	- name: Dev WER (+LM)
	type: wer
	value: 26.6
	- name: Dev CER (+LM)
	type: cer
	value: 12.58
	---

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

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice).
	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-german")
	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 = "de"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-german"
	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 \|
	\| ------------- \| ------------- \|
	\| ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS. \| ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS \|
	\| ES KOMMT ZUM SHOWDOWN IN GSTAAD. \| ES KOMMT ZUG STUNDEDAUTENESTERKT \|
	\| IHRE FOTOSTRECKEN ERSCHIENEN IN MODEMAGAZINEN WIE DER VOGUE, HARPER’S BAZAAR UND MARIE CLAIRE. \| IHRE FOTELSTRECKEN ERSCHIENEN MIT MODEMAGAZINEN WIE DER VALG AT DAS BASIN MA RIQUAIR \|
	\| FELIPE HAT EINE AUCH FÜR MONARCHEN UNGEWÖHNLICH LANGE TITELLISTE. \| FELIPPE HAT EINE AUCH FÜR MONACHEN UNGEWÖHNLICH LANGE TITELLISTE \|
	\| ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET. \| ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET M \|
	\| WAS SOLLS, ICH BIN BEREIT. \| WAS SOLL'S ICH BIN BEREIT \|
	\| DAS INTERNET BESTEHT AUS VIELEN COMPUTERN, DIE MITEINANDER VERBUNDEN SIND. \| DAS INTERNET BESTEHT AUS VIELEN COMPUTERN DIE MITEINANDER VERBUNDEN SIND \|
	\| DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM. \| DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM \|
	\| DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND. \| DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND \|
	\| SIE WAR DIE COUSINE VON CARL MARIA VON WEBER. \| SIE WAR DIE COUSINE VON KARL-MARIA VON WEBER \|

	## 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-german --dataset mozilla-foundation/common_voice_6_0 --config de --split test
	```

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

	```bash
	python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-german --dataset speech-recognition-community-v2/dev_data --config de --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-german,
	title={Fine-tuned {XLSR}-53 large model for speech recognition in {G}erman},
	author={Grosman, Jonatas},
	howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german}},
	year={2021}
	}
	```