metadata

language: pl
license: apache-2.0
datasets:
  - common_voice
  - mozilla-foundation/common_voice_6_0
metrics:
  - wer
  - cer
tags:
  - pl
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  - robust-speech-event
  - mozilla-foundation/common_voice_6_0
model-index:
  - name: XLSR Wav2Vec2 Polish by Jonatas Grosman
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice pl
          type: common_voice
          args: pl
        metrics:
          - name: Test WER
            type: wer
            value: 14.21
          - name: Test CER
            type: cer
            value: 3.49
          - name: Test WER (+LM)
            type: wer
            value: 10.98
          - name: Test CER (+LM)
            type: cer
            value: 2.93
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: pl
        metrics:
          - name: Dev WER
            type: wer
            value: 33.18
          - name: Dev CER
            type: cer
            value: 15.92
          - name: Dev WER (+LM)
            type: wer
            value: 29.31
          - name: Dev CER (+LM)
            type: cer
            value: 15.17

Wav2Vec2-Large-XLSR-53-Polish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the 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 :)

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 ASRecognition library:

from asrecognition import ASREngine

asr = ASREngine("pl", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-polish")

audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)

Writing your own inference script:

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

LANG_ID = "pl"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-polish"
SAMPLES = 5

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
"""CZY DRZWI BYŁY ZAMKNIĘTE?"""	PRZY DRZWI BYŁY ZAMKNIĘTE
GDZIEŻ TU POWÓD DO WYRZUTÓW?	WGDZIEŻ TO POM DO WYRYDÓ
"""O TEM JEDNAK NIE BYŁO MOWY."""	O TEM JEDNAK NIE BYŁO MOWY
LUBIĘ GO.	LUBIĄ GO
— TO MI NIE POMAGA.	TO MNIE NIE POMAGA
WCIĄŻ LUDZIE WYSIADAJĄ PRZED ZAMKIEM, Z MIASTA, Z PRAGI.	WCIĄŻ LUDZIE WYSIADAJĄ PRZED ZAMKIEM Z MIASTA Z PRAGI
ALE ON WCALE INACZEJ NIE MYŚLAŁ.	ONY MONITCENIE PONACZUŁA NA MASU
A WY, CO TAK STOICIE?	A WY CO TAK STOICIE
A TEN PRZYRZĄD DO CZEGO SŁUŻY?	A TEN PRZYRZĄD DO CZEGO SŁUŻY
NA JUTRZEJSZYM KOLOKWIUM BĘDZIE PIĘĆ PYTAŃ OTWARTYCH I TEST WIELOKROTNEGO WYBORU.	NAJUTRZEJSZYM KOLOKWIUM BĘDZIE PIĘĆ PYTAŃ OTWARTYCH I TEST WIELOKROTNEGO WYBORU

Evaluation

To evaluate on mozilla-foundation/common_voice_6_0 with split test

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

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

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