README.md

metadata

language: sv-SE
datasets:
  - common_voice
  - NST Swedish ASR Database
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - speech
  - voxpopuli
license: cc-by-nc-4.0
model-index:
  - name: Wav2vec 2.0 base VoxPopuli-sv swedish
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: NST Swedish ASR Database
        metrics:
          - name: Test WER
            type: wer
            value: 5.619804368919309
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice
          type: common_voice
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 19.145252414798616

Wav2vec 2.0 base-voxpopuli-sv-swedish

Finetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.

When using this model, make sure that your speech input is sampled at 16kHz.

Usage

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

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-base-voxpopuli-sv-swedish")
model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-base-voxpopuli-sv-swedish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], 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)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])