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xls-r-300m-sv

Automatic Speech Recognition
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PyTorch
TensorBoard
wav2vec2
Generated from Trainer
hf-asr-leaderboard
hello
model_for_talk
mozilla-foundation/common_voice_7_0
robust-speech-event
sv
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metadata
language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

XLS-R-300m-SV

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3171
  • Wer: 0.2730

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3349 1.45 500 3.2858 1.0
2.9298 2.91 1000 2.9225 1.0000
2.0839 4.36 1500 1.1546 0.8295
1.7093 5.81 2000 0.6827 0.5701
1.5855 7.27 2500 0.5597 0.4947
1.4831 8.72 3000 0.4923 0.4527
1.4416 10.17 3500 0.4670 0.4270
1.3848 11.63 4000 0.4341 0.3980
1.3749 13.08 4500 0.4203 0.4011
1.3311 14.53 5000 0.4310 0.3961
1.317 15.99 5500 0.3898 0.4322
1.2799 17.44 6000 0.3806 0.3572
1.2771 18.89 6500 0.3828 0.3427
1.2451 20.35 7000 0.3702 0.3359
1.2182 21.8 7500 0.3685 0.3270
1.2152 23.26 8000 0.3650 0.3308
1.1837 24.71 8500 0.3568 0.3187
1.1721 26.16 9000 0.3659 0.3249
1.1764 27.61 9500 0.3547 0.3145
1.1606 29.07 10000 0.3514 0.3104
1.1431 30.52 10500 0.3469 0.3062
1.1047 31.97 11000 0.3313 0.2979
1.1315 33.43 11500 0.3298 0.2992
1.1022 34.88 12000 0.3296 0.2973
1.0935 36.34 12500 0.3278 0.2926
1.0676 37.79 13000 0.3208 0.2868
1.0571 39.24 13500 0.3322 0.2885
1.0536 40.7 14000 0.3245 0.2831
1.0525 42.15 14500 0.3285 0.2826
1.0464 43.6 15000 0.3223 0.2796
1.0415 45.06 15500 0.3166 0.2774
1.0356 46.51 16000 0.3177 0.2746
1.04 47.96 16500 0.3150 0.2735
1.0209 49.42 17000 0.3175 0.2731

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.10.3

Inference With LM 

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F


model_id = "hf-test/xls-r-300m-sv"

sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "sv-SE", split="test", streaming=True, use_auth_token=True))

sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()

model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)

input_values = processor(resampled_audio, return_tensors="pt").input_values

with torch.no_grad():
    logits = model(input_values).logits

transcription = processor.batch_decode(logits.numpy()).text
# => "jag lämnade grovjobbet åt honom"

Eval results on Common Voice 7 "test" (WER):

Without LM With LM (run ./eval.py)
27.30 18.85