imvladikon_wav2vec2-xls-r-300m-hebrew

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language:

he tags:
automatic-speech-recognition
generated_from_trainer
he
hf-asr-leaderboard
robust-speech-event base_model: facebook/wav2vec2-xls-r-300m model-index:
name: wav2vec2-xls-r-300m-hebrew results:
- task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Custom Dataset type: custom args: he metrics:
  - type: wer value: 23.18 name: Test WER

wav2vec2-xls-r-300m-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model.

Small dataset:

split	size(gb)	n_samples	duration(hrs)
train	4.19	20306	28
dev	1.05	5076	7

Large dataset:

split	size(gb)	n_samples	duration(hrs)
train	12.3	90777	69
dev	2.39	20246	14*
(*weakly labeled data wasn't used in validation set)

After firts training it achieves:

on small dataset

Loss: 0.5438
WER: 0.1773

on large dataset

WER: 0.3811

after second training: on small dataset

WER: 0.1697

on large dataset

Loss: 0.4502
WER: 0.2318

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

First training

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	3.15	1000	0.5203	0.4333
1.4284	6.31	2000	0.4816	0.3951
1.4284	9.46	3000	0.4315	0.3546
1.283	12.62	4000	0.4278	0.3404
1.283	15.77	5000	0.4090	0.3054
1.1777	18.93	6000	0.3893	0.3006
1.1777	22.08	7000	0.3968	0.2857
1.0994	25.24	8000	0.3892	0.2751
1.0994	28.39	9000	0.4061	0.2690
1.0323	31.54	10000	0.4114	0.2507
1.0323	34.7	11000	0.4021	0.2508
0.9623	37.85	12000	0.4032	0.2378
0.9623	41.01	13000	0.4148	0.2374
0.9077	44.16	14000	0.4350	0.2323
0.9077	47.32	15000	0.4515	0.2246
0.8573	50.47	16000	0.4474	0.2180
0.8573	53.63	17000	0.4649	0.2171
0.8083	56.78	18000	0.4455	0.2102
0.8083	59.94	19000	0.4587	0.2092
0.769	63.09	20000	0.4794	0.2012
0.769	66.25	21000	0.4845	0.2007
0.7308	69.4	22000	0.4937	0.2008
0.7308	72.55	23000	0.4920	0.1895
0.6927	75.71	24000	0.5179	0.1911
0.6927	78.86	25000	0.5202	0.1877
0.6622	82.02	26000	0.5266	0.1840
0.6622	85.17	27000	0.5351	0.1854
0.6315	88.33	28000	0.5373	0.1811
0.6315	91.48	29000	0.5331	0.1792
0.6075	94.64	30000	0.5390	0.1779
0.6075	97.79	31000	0.5459	0.1773

Second training

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 60.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.7	1000	0.5371	0.3811
1.3606	1.41	2000	0.5247	0.3902
1.3606	2.12	3000	0.5126	0.3859
1.3671	2.82	4000	0.5062	0.3828
1.3671	3.53	5000	0.4979	0.3672
1.3421	4.23	6000	0.4906	0.3816
1.3421	4.94	7000	0.4784	0.3651
1.328	5.64	8000	0.4810	0.3669
1.328	6.35	9000	0.4747	0.3597
1.3109	7.05	10000	0.4813	0.3808
1.3109	7.76	11000	0.4631	0.3561
1.2873	8.46	12000	0.4603	0.3431
1.2873	9.17	13000	0.4579	0.3533
1.2661	9.87	14000	0.4471	0.3365
1.2661	10.58	15000	0.4584	0.3437
1.249	11.28	16000	0.4461	0.3454
1.249	11.99	17000	0.4482	0.3367
1.2322	12.69	18000	0.4464	0.3335
1.2322	13.4	19000	0.4427	0.3454
1.22	14.1	20000	0.4440	0.3395
1.22	14.81	21000	0.4459	0.3378
1.2044	15.51	22000	0.4406	0.3199
1.2044	16.22	23000	0.4398	0.3155
1.1913	16.92	24000	0.4237	0.3150
1.1913	17.63	25000	0.4287	0.3279
1.1705	18.34	26000	0.4253	0.3103
1.1705	19.04	27000	0.4234	0.3098
1.1564	19.75	28000	0.4174	0.3076
1.1564	20.45	29000	0.4260	0.3160
1.1461	21.16	30000	0.4235	0.3036
1.1461	21.86	31000	0.4309	0.3055
1.1285	22.57	32000	0.4264	0.3006
1.1285	23.27	33000	0.4201	0.2880
1.1135	23.98	34000	0.4131	0.2975
1.1135	24.68	35000	0.4202	0.2849
1.0968	25.39	36000	0.4105	0.2888
1.0968	26.09	37000	0.4210	0.2834
1.087	26.8	38000	0.4123	0.2843
1.087	27.5	39000	0.4216	0.2803
1.0707	28.21	40000	0.4161	0.2787
1.0707	28.91	41000	0.4186	0.2740
1.0575	29.62	42000	0.4118	0.2845
1.0575	30.32	43000	0.4243	0.2773
1.0474	31.03	44000	0.4221	0.2707
1.0474	31.73	45000	0.4138	0.2700
1.0333	32.44	46000	0.4102	0.2638
1.0333	33.15	47000	0.4162	0.2650
1.0191	33.85	48000	0.4155	0.2636
1.0191	34.56	49000	0.4129	0.2656
1.0087	35.26	50000	0.4157	0.2632
1.0087	35.97	51000	0.4090	0.2654
0.9901	36.67	52000	0.4183	0.2587
0.9901	37.38	53000	0.4251	0.2648
0.9795	38.08	54000	0.4229	0.2555
0.9795	38.79	55000	0.4176	0.2546
0.9644	39.49	56000	0.4223	0.2513
0.9644	40.2	57000	0.4244	0.2530
0.9534	40.9	58000	0.4175	0.2538
0.9534	41.61	59000	0.4213	0.2505
0.9397	42.31	60000	0.4275	0.2565
0.9397	43.02	61000	0.4315	0.2528
0.9269	43.72	62000	0.4316	0.2501
0.9269	44.43	63000	0.4247	0.2471
0.9175	45.13	64000	0.4376	0.2469
0.9175	45.84	65000	0.4335	0.2450
0.9026	46.54	66000	0.4336	0.2452
0.9026	47.25	67000	0.4400	0.2427
0.8929	47.95	68000	0.4382	0.2429
0.8929	48.66	69000	0.4361	0.2415
0.8786	49.37	70000	0.4413	0.2398
0.8786	50.07	71000	0.4392	0.2415
0.8714	50.78	72000	0.4345	0.2406
0.8714	51.48	73000	0.4475	0.2402
0.8589	52.19	74000	0.4473	0.2374
0.8589	52.89	75000	0.4457	0.2357
0.8493	53.6	76000	0.4462	0.2366
0.8493	54.3	77000	0.4494	0.2356
0.8395	55.01	78000	0.4472	0.2352
0.8395	55.71	79000	0.4490	0.2339
0.8295	56.42	80000	0.4489	0.2318
0.8295	57.12	81000	0.4469	0.2320
0.8225	57.83	82000	0.4478	0.2321
0.8225	58.53	83000	0.4525	0.2326
0.816	59.24	84000	0.4532	0.2316
0.816	59.94	85000	0.4502	0.2318

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0

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Files & hashes

Path	Size	sha1	sha256
README.md	10.1 KB (10,351 B)	86115930af59ffc17367b316e9b4bfa2c6156fbc	f454473146195ec85f1258f355759e4495078e7fe0e1eb0374dc46b49b164646
added_tokens.json	23 B (23 B)	2dc407ef9585810c95e2eb479b16366e4a6c64ff	fab5e2d4f8deb5acbdb10f1e3af9a58961c74a532cd51905bf958cba0a404854
all_results.json	403 B (403 B)	acb8f5e66efd7592d544129ad7bd55b92913578e	6fcaa321b98e8499110bbdc5ec26e481fb4933d68bbc9404cec0201986795967
config.json	2.0 KB (2,042 B)	df05220507566e5d7a3651526a13ec9705a8c337	7f6fb18feda97b96c8c7a11dd4c613b27a195367ab4ba837a4ed43b25b133ada
eval.py	4.8 KB (4,878 B)	88bfe8f701fb56c1d89710b9765c2e8e32f8730d	4edbb069ed5a7603ba0e19c0c7733796ab89c9729dd69babdb14f5e80cffedc2
model.safetensors	1.18 GB (1,261,938,632 B)	37fd612f760dca30951c216e6866ec242a9d14ac	75c4e535a62575ac57cf374dec17fb72fbab973dbd69d0391e47918880cf4e00
preprocessor_config.json	214 B (214 B)	73caa151574001d3d495fae897e1d38968249712	60ca5a31e13f69ee2fbf147504c8676db5f6398fd7a6b12294341dff838edfcf
pytorch_model.bin	1.18 GB (1,262,054,897 B)	3fa1e349f55586b80b9c40cd819e190951e85dc9	5f4ee7fbf1fd8b43c413809f06de6b07930c451e471decd5d7219a2704dee64c
run_train.py	35.6 KB (36,437 B)	07b12609094f4b48347c29db808ee8d1914cb7f5	3d2e0de7404f09a74dd4a88f7ff10eecdf9ef4c76350bea90ea4722faca9b8cb
run_train.sh	1.2 KB (1,213 B)	9375ab46d60a34c32d8dbc106153148a0425122e	07857a0d1f5fa6a922b61e2477efc332c5128921564255db9fb2d9d0de1a354d
special_tokens_map.json	1.2 KB (1,274 B)	258236c891c120aced9b42c674e4c8fd6116d2a7	33238ee53fdde916cdd7508a659dba61badd77ded8809a7aae99788eefa11aa2
tokenizer_config.json	288 B (288 B)	2a4a5c3787f0b10e94ddd006e74e4c7c7eb6ef4c	268f84789f1032644f6ac76d43335a0fd85acd09d87de874158ac7b47df44ef9
train_results.json	197 B (197 B)	3cef30e39406a289f840bfb8e97338ea4e036df1	b7122cb757844db483ba141b30a02a9ca6513fff9f77b04262a0e418ba9fb75b
trainer_state.json	25.8 KB (26,448 B)	3d39585f36f7215252a3cd2bbd99358a80b7cbd0	b077dc58484c6f085de8f4d8cff3b4039c97a834da1ad8d78e30d8e15d9e80e6
training_args.bin	3.0 KB (3,055 B)	7efb631eadba3d01c86cbc3f686174c27f76a497	dfeb10e403369a185fe8d387f3aab04ec2ddef0dea3b5d4d22baea6101fdec23
validation_results.json	227 B (227 B)	0a84c454ac3133841c2f0fe7f091a62a1c4189e6	3c2badb99ec95c7f722048260a6b803172d3baa47ad22d55be89f95159b89e2c
vocab.json	295 B (295 B)	eb991cf44f6ce71c8acfa50fca6d2fba8dd4b17e	b8d588aed425f2be4aa84d18ec2ecc0d9dffc4b35577ab15c2d7737f30eb0efe

Cite this release

Canonical URL: https://aiseedbank.org/models/imvladikon_wav2vec2-xls-r-300m-hebrew/
Slug: imvladikon_wav2vec2-xls-r-300m-hebrew
Infohash: 7a2eb4592995cd46d6403ed2727474d60db997b8
License: no license recorded
Signing key fingerprint: 85a3b32c3712427b

Every file carries a locally computed sha256 — verify a download against the signed sums: imvladikon_wav2vec2-xls-r-300m-hebrew.SHA256SUMS (+ minisign signature).

Provenance

Upstream repository	imvladikon/wav2vec2-xls-r-300m-hebrew
Revision (pinned)	b2e683004903f1b11bee2c6092a7c323ed858d82
Fetched at	2026-09-04T00:53:54Z
License at fetch	no license recorded
Snapshot tool	huggingface · seedbank 0.1.0

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✓ verified · rehash-vs-hf-metadata at 2026-09-04T00:54:29Z

no license recorded2.35 GB (2,524,080,874 bytes)transformerspytorchsafetensorswav2vec2automatic-speech-recognitiongenerated_from_trainerhf-asr-leaderboardrobust-speech-eventmodel-indexendpoints_compatible1 language (he)