README.md

metadata

language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - sv
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Swedish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 16.5
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 18.85
          - name: Test CER
            type: cer
            value: 6.6

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