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# 

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - ZH-CN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8122
- Wer: 0.8392
- Cer: 0.2059

## 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: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 69.215        | 0.74  | 500   | 74.9751         | 1.0    | 1.0    |
| 8.2109        | 1.48  | 1000  | 7.0617          | 1.0    | 1.0    |
| 6.4277        | 2.22  | 1500  | 6.3811          | 1.0    | 1.0    |
| 6.3513        | 2.95  | 2000  | 6.3061          | 1.0    | 1.0    |
| 6.2522        | 3.69  | 2500  | 6.2147          | 1.0    | 1.0    |
| 5.9757        | 4.43  | 3000  | 5.7906          | 1.1004 | 0.9924 |
| 5.0642        | 5.17  | 3500  | 4.2984          | 1.7729 | 0.8214 |
| 4.6346        | 5.91  | 4000  | 3.7129          | 1.8946 | 0.7728 |
| 4.267         | 6.65  | 4500  | 3.2177          | 1.7526 | 0.6922 |
| 3.9964        | 7.39  | 5000  | 2.8337          | 1.8055 | 0.6546 |
| 3.8035        | 8.12  | 5500  | 2.5726          | 2.1851 | 0.6992 |
| 3.6273        | 8.86  | 6000  | 2.3391          | 2.1029 | 0.6511 |
| 3.5248        | 9.6   | 6500  | 2.1944          | 2.3617 | 0.6859 |
| 3.3683        | 10.34 | 7000  | 1.9827          | 2.1014 | 0.6063 |
| 3.2411        | 11.08 | 7500  | 1.8610          | 1.6160 | 0.5135 |
| 3.1299        | 11.82 | 8000  | 1.7446          | 1.5948 | 0.4946 |
| 3.0574        | 12.56 | 8500  | 1.6454          | 1.1291 | 0.4051 |
| 2.985         | 13.29 | 9000  | 1.5919          | 1.0673 | 0.3893 |
| 2.9573        | 14.03 | 9500  | 1.4903          | 1.0604 | 0.3766 |
| 2.8897        | 14.77 | 10000 | 1.4614          | 1.0059 | 0.3653 |
| 2.8169        | 15.51 | 10500 | 1.3997          | 1.0030 | 0.3550 |
| 2.8155        | 16.25 | 11000 | 1.3444          | 0.9980 | 0.3441 |
| 2.7595        | 16.99 | 11500 | 1.2911          | 0.9703 | 0.3325 |
| 2.7107        | 17.72 | 12000 | 1.2462          | 0.9565 | 0.3227 |
| 2.6358        | 18.46 | 12500 | 1.2466          | 0.9955 | 0.3333 |
| 2.5801        | 19.2  | 13000 | 1.2059          | 1.0010 | 0.3226 |
| 2.5554        | 19.94 | 13500 | 1.1919          | 1.0094 | 0.3223 |
| 2.5314        | 20.68 | 14000 | 1.1703          | 0.9847 | 0.3156 |
| 2.509         | 21.42 | 14500 | 1.1733          | 0.9896 | 0.3177 |
| 2.4391        | 22.16 | 15000 | 1.1811          | 0.9723 | 0.3164 |
| 2.4631        | 22.89 | 15500 | 1.1382          | 0.9698 | 0.3059 |
| 2.4414        | 23.63 | 16000 | 1.0893          | 0.9644 | 0.2972 |
| 2.3771        | 24.37 | 16500 | 1.0930          | 0.9505 | 0.2954 |
| 2.3658        | 25.11 | 17000 | 1.0756          | 0.9609 | 0.2926 |
| 2.3215        | 25.85 | 17500 | 1.0512          | 0.9614 | 0.2890 |
| 2.3327        | 26.59 | 18000 | 1.0627          | 1.1984 | 0.3282 |
| 2.3055        | 27.33 | 18500 | 1.0582          | 0.9520 | 0.2841 |
| 2.299         | 28.06 | 19000 | 1.0356          | 0.9480 | 0.2817 |
| 2.2673        | 28.8  | 19500 | 1.0305          | 0.9367 | 0.2771 |
| 2.2166        | 29.54 | 20000 | 1.0139          | 0.9223 | 0.2702 |
| 2.2378        | 30.28 | 20500 | 1.0095          | 0.9268 | 0.2722 |
| 2.2168        | 31.02 | 21000 | 1.0001          | 0.9085 | 0.2691 |
| 2.1766        | 31.76 | 21500 | 0.9884          | 0.9050 | 0.2640 |
| 2.1715        | 32.5  | 22000 | 0.9730          | 0.9505 | 0.2719 |
| 2.1104        | 33.23 | 22500 | 0.9752          | 0.9362 | 0.2656 |
| 2.1158        | 33.97 | 23000 | 0.9720          | 0.9263 | 0.2624 |
| 2.0718        | 34.71 | 23500 | 0.9573          | 1.0005 | 0.2759 |
| 2.0824        | 35.45 | 24000 | 0.9609          | 0.9525 | 0.2643 |
| 2.0591        | 36.19 | 24500 | 0.9662          | 0.9570 | 0.2667 |
| 2.0768        | 36.93 | 25000 | 0.9528          | 0.9574 | 0.2646 |
| 2.0893        | 37.67 | 25500 | 0.9810          | 0.9169 | 0.2612 |
| 2.0282        | 38.4  | 26000 | 0.9556          | 0.8877 | 0.2528 |
| 1.997         | 39.14 | 26500 | 0.9523          | 0.8723 | 0.2501 |
| 2.0209        | 39.88 | 27000 | 0.9542          | 0.8773 | 0.2503 |
| 1.987         | 40.62 | 27500 | 0.9427          | 0.8867 | 0.2500 |
| 1.9663        | 41.36 | 28000 | 0.9546          | 0.9065 | 0.2546 |
| 1.9945        | 42.1  | 28500 | 0.9431          | 0.9119 | 0.2536 |
| 1.9604        | 42.84 | 29000 | 0.9367          | 0.9030 | 0.2490 |
| 1.933         | 43.57 | 29500 | 0.9071          | 0.8916 | 0.2432 |
| 1.9227        | 44.31 | 30000 | 0.9048          | 0.8882 | 0.2428 |
| 1.8784        | 45.05 | 30500 | 0.9106          | 0.8991 | 0.2437 |
| 1.8844        | 45.79 | 31000 | 0.8996          | 0.8758 | 0.2379 |
| 1.8776        | 46.53 | 31500 | 0.9028          | 0.8798 | 0.2395 |
| 1.8372        | 47.27 | 32000 | 0.9047          | 0.8778 | 0.2379 |
| 1.832         | 48.01 | 32500 | 0.9016          | 0.8941 | 0.2393 |
| 1.8154        | 48.74 | 33000 | 0.8915          | 0.8916 | 0.2372 |
| 1.8072        | 49.48 | 33500 | 0.8781          | 0.8872 | 0.2365 |
| 1.7489        | 50.22 | 34000 | 0.8738          | 0.8956 | 0.2340 |
| 1.7928        | 50.96 | 34500 | 0.8684          | 0.8872 | 0.2323 |
| 1.7748        | 51.7  | 35000 | 0.8723          | 0.8718 | 0.2321 |
| 1.7355        | 52.44 | 35500 | 0.8760          | 0.8842 | 0.2331 |
| 1.7167        | 53.18 | 36000 | 0.8746          | 0.8817 | 0.2324 |
| 1.7479        | 53.91 | 36500 | 0.8762          | 0.8753 | 0.2281 |
| 1.7428        | 54.65 | 37000 | 0.8733          | 0.8699 | 0.2277 |
| 1.7058        | 55.39 | 37500 | 0.8816          | 0.8649 | 0.2263 |
| 1.7045        | 56.13 | 38000 | 0.8733          | 0.8689 | 0.2297 |
| 1.709         | 56.87 | 38500 | 0.8648          | 0.8654 | 0.2232 |
| 1.6799        | 57.61 | 39000 | 0.8717          | 0.8580 | 0.2244 |
| 1.664         | 58.35 | 39500 | 0.8653          | 0.8723 | 0.2259 |
| 1.6488        | 59.08 | 40000 | 0.8637          | 0.8803 | 0.2271 |
| 1.6298        | 59.82 | 40500 | 0.8553          | 0.8768 | 0.2253 |
| 1.6185        | 60.56 | 41000 | 0.8512          | 0.8718 | 0.2240 |
| 1.574         | 61.3  | 41500 | 0.8579          | 0.8773 | 0.2251 |
| 1.6192        | 62.04 | 42000 | 0.8499          | 0.8743 | 0.2242 |
| 1.6275        | 62.78 | 42500 | 0.8419          | 0.8758 | 0.2216 |
| 1.5697        | 63.52 | 43000 | 0.8446          | 0.8699 | 0.2222 |
| 1.5384        | 64.25 | 43500 | 0.8462          | 0.8580 | 0.2200 |
| 1.5115        | 64.99 | 44000 | 0.8467          | 0.8674 | 0.2214 |
| 1.5547        | 65.73 | 44500 | 0.8505          | 0.8669 | 0.2204 |
| 1.5597        | 66.47 | 45000 | 0.8421          | 0.8684 | 0.2192 |
| 1.505         | 67.21 | 45500 | 0.8485          | 0.8619 | 0.2187 |
| 1.5101        | 67.95 | 46000 | 0.8489          | 0.8649 | 0.2204 |
| 1.5199        | 68.69 | 46500 | 0.8407          | 0.8619 | 0.2180 |
| 1.5207        | 69.42 | 47000 | 0.8379          | 0.8496 | 0.2163 |
| 1.478         | 70.16 | 47500 | 0.8357          | 0.8595 | 0.2163 |
| 1.4817        | 70.9  | 48000 | 0.8346          | 0.8496 | 0.2151 |
| 1.4827        | 71.64 | 48500 | 0.8362          | 0.8624 | 0.2169 |
| 1.4513        | 72.38 | 49000 | 0.8355          | 0.8451 | 0.2137 |
| 1.4988        | 73.12 | 49500 | 0.8325          | 0.8624 | 0.2161 |
| 1.4267        | 73.85 | 50000 | 0.8396          | 0.8481 | 0.2157 |
| 1.4421        | 74.59 | 50500 | 0.8355          | 0.8491 | 0.2122 |
| 1.4311        | 75.33 | 51000 | 0.8358          | 0.8476 | 0.2118 |
| 1.4174        | 76.07 | 51500 | 0.8289          | 0.8451 | 0.2101 |
| 1.4349        | 76.81 | 52000 | 0.8372          | 0.8580 | 0.2140 |
| 1.3959        | 77.55 | 52500 | 0.8325          | 0.8436 | 0.2116 |
| 1.4087        | 78.29 | 53000 | 0.8351          | 0.8446 | 0.2105 |
| 1.415         | 79.03 | 53500 | 0.8363          | 0.8476 | 0.2123 |
| 1.4122        | 79.76 | 54000 | 0.8310          | 0.8481 | 0.2112 |
| 1.3969        | 80.5  | 54500 | 0.8239          | 0.8446 | 0.2095 |
| 1.361         | 81.24 | 55000 | 0.8282          | 0.8427 | 0.2091 |
| 1.3611        | 81.98 | 55500 | 0.8282          | 0.8407 | 0.2092 |
| 1.3677        | 82.72 | 56000 | 0.8235          | 0.8436 | 0.2084 |
| 1.3361        | 83.46 | 56500 | 0.8231          | 0.8377 | 0.2069 |
| 1.3779        | 84.19 | 57000 | 0.8206          | 0.8436 | 0.2070 |
| 1.3727        | 84.93 | 57500 | 0.8204          | 0.8392 | 0.2065 |
| 1.3317        | 85.67 | 58000 | 0.8207          | 0.8436 | 0.2065 |
| 1.3332        | 86.41 | 58500 | 0.8186          | 0.8357 | 0.2055 |
| 1.3299        | 87.15 | 59000 | 0.8193          | 0.8417 | 0.2075 |
| 1.3129        | 87.89 | 59500 | 0.8183          | 0.8431 | 0.2065 |
| 1.3352        | 88.63 | 60000 | 0.8151          | 0.8471 | 0.2062 |
| 1.3026        | 89.36 | 60500 | 0.8125          | 0.8486 | 0.2067 |
| 1.3468        | 90.1  | 61000 | 0.8124          | 0.8407 | 0.2058 |
| 1.3028        | 90.84 | 61500 | 0.8122          | 0.8461 | 0.2051 |
| 1.2884        | 91.58 | 62000 | 0.8086          | 0.8427 | 0.2048 |
| 1.3005        | 92.32 | 62500 | 0.8110          | 0.8387 | 0.2055 |
| 1.2996        | 93.06 | 63000 | 0.8126          | 0.8328 | 0.2057 |
| 1.2707        | 93.8  | 63500 | 0.8098          | 0.8402 | 0.2047 |
| 1.3026        | 94.53 | 64000 | 0.8097          | 0.8402 | 0.2050 |
| 1.2546        | 95.27 | 64500 | 0.8111          | 0.8402 | 0.2055 |
| 1.2426        | 96.01 | 65000 | 0.8088          | 0.8372 | 0.2059 |
| 1.2869        | 96.75 | 65500 | 0.8093          | 0.8397 | 0.2048 |
| 1.2782        | 97.49 | 66000 | 0.8099          | 0.8412 | 0.2049 |
| 1.2457        | 98.23 | 66500 | 0.8134          | 0.8412 | 0.2062 |
| 1.2967        | 98.97 | 67000 | 0.8115          | 0.8382 | 0.2055 |
| 1.2817        | 99.7  | 67500 | 0.8128          | 0.8392 | 0.2063 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0

{"language": ["zh-CN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "sv"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "zh-CN"}, "metrics": [{"type": "cer", "value": 66.22, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "zh-CN"}, "metrics": [{"type": "cer", "value": 37.51, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer hf-asr-leaderboard robust-speech-event sv dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - ZH-CN dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8122
* Wer: 0.8392
* Cer: 0.2059


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: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0



### 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: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
### 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: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0