jonatasgrosman_wav2vec2-large-xlsr-53-german

---

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 on German using the train and validation splits of Common Voice 6.1. 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 :)

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 library:

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:

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

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-german --dataset mozilla-foundation/common_voice_6_0 --config de --split test

1. To evaluate on speech-recognition-community-v2/dev_data

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:

@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}
}