Model reference · open weights

wav2vec2-large-xls-r-bg-d2

Name: wav2vec2-large-xls-r-bg-d2
Brand: AxForge

Available as managed deployment Audio DrishtiSharma · community Speech→text 1 variants 403k dl/mo

wav2vec2-large-xls-r-bg-d2 is an open-weight audio or speech model from DrishtiSharma. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

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Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released by	DrishtiSharma
Type	Audio & music
Task	Speech→text
Runs with	transformers
Released	2022-03-02
Popularity	403k downloads / month
Licence	Open weights

About

What wav2vec2-large-xls-r-bg-d2 is

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set:

Loss: 0.3421
Wer: 0.2860

Read the full model card

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs

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

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00025
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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: 700
num_epochs: 35
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.8791	1.74	200	3.1902	1.0
3.0441	3.48	400	2.8098	0.9864
1.1499	5.22	600	0.4668	0.5014
0.4968	6.96	800	0.4162	0.4472
0.3553	8.7	1000	0.3580	0.3777
0.3027	10.43	1200	0.3422	0.3506
0.2562	12.17	1400	0.3556	0.3639
0.2272	13.91	1600	0.3621	0.3583
0.2125	15.65	1800	0.3436	0.3358
0.1904	17.39	2000	0.3650	0.3545
0.1695	19.13	2200	0.3366	0.3241
0.1532	20.87	2400	0.3550	0.3311
0.1453	22.61	2600	0.3582	0.3131
0.1359	24.35	2800	0.3524	0.3084
0.1233	26.09	3000	0.3503	0.2973
0.1114	27.83	3200	0.3434	0.2946
0.1051	29.57	3400	0.3474	0.2956
0.0965	31.3	3600	0.3426	0.2907
0.0923	33.04	3800	0.3478	0.2894
0.0894	34.78	4000	0.3421	0.2860

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0

From the published model card. Full card on the HuggingFace links in the sidebar.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Task	Dataset	Metric	Score
Automatic Speech Recognition	Common Voice 8	Test WER	0.288
Automatic Speech Recognition	Common Voice 8	Test CER	0.069
Automatic Speech Recognition	Robust Speech Event - Dev Data	Test WER	0.498
Automatic Speech Recognition	Robust Speech Event - Dev Data	Test CER	0.159
Automatic Speech Recognition	Robust Speech Event - Test Data	Test WER	51.250

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys wav2vec2-large-xls-r-bg-d2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (wav2vec2-large-xls-r-bg-d2 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="wav2vec2-large-xls-r-bg-d2" -F file=@audio.mp3

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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Sizes, precisions & builds

wav2vec2-large-xls-r-300m-bg-d2

— · BF16

Weights ↗

Details

Languages

Trained / evaluated on

mozilla-foundation/common_voice_8_0