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# ft_1

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
- Cer: 0.0011

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 9.6728        | 0.08  | 500    | 0.9218          | 1.0    |
| 0.6369        | 0.17  | 1000   | 0.6685          | 1.0    |
| 0.511         | 0.25  | 1500   | 0.4867          | 0.5464 |
| 0.4632        | 0.34  | 2000   | 0.4821          | 0.2281 |
| 0.4354        | 0.42  | 2500   | 0.3887          | 0.1763 |
| 0.3664        | 0.51  | 3000   | 0.2324          | 0.0484 |
| 0.2641        | 0.59  | 3500   | 0.1729          | 0.0276 |
| 0.218         | 0.67  | 4000   | 0.1485          | 0.0213 |
| 0.1938        | 0.76  | 4500   | 0.1270          | 0.0188 |
| 0.1867        | 0.84  | 5000   | 0.1192          | 0.0177 |
| 0.1756        | 0.93  | 5500   | 0.1047          | 0.0163 |
| 0.1659        | 1.01  | 6000   | 0.1008          | 0.0150 |
| 0.1522        | 1.1   | 6500   | 0.0970          | 0.0140 |
| 0.1456        | 1.18  | 7000   | 0.0944          | 0.0140 |
| 0.1431        | 1.26  | 7500   | 0.0905          | 0.0134 |
| 0.1416        | 1.35  | 8000   | 0.0919          | 0.0120 |
| 0.1356        | 1.43  | 8500   | 0.0792          | 0.0124 |
| 0.1345        | 1.52  | 9000   | 0.0880          | 0.0129 |
| 0.1316        | 1.6   | 9500   | 0.0753          | 0.0111 |
| 0.1293        | 1.69  | 10000  | 0.0728          | 0.0106 |
| 0.1238        | 1.77  | 10500  | 0.0682          | 0.0109 |
| 0.121         | 1.85  | 11000  | 0.0645          | 0.0100 |
| 0.1161        | 1.94  | 11500  | 0.0658          | 0.0094 |
| 0.1107        | 2.02  | 12000  | 0.0669          | 0.0115 |
| 0.1054        | 2.11  | 12500  | 0.0597          | 0.0093 |
| 0.104         | 2.19  | 13000  | 0.0572          | 0.0088 |
| 0.1007        | 2.28  | 13500  | 0.0522          | 0.0082 |
| 0.0986        | 2.36  | 14000  | 0.0575          | 0.0090 |
| 0.103         | 2.44  | 14500  | 0.0513          | 0.0085 |
| 0.0973        | 2.53  | 15000  | 0.0597          | 0.0081 |
| 0.0949        | 2.61  | 15500  | 0.0474          | 0.0074 |
| 0.092         | 2.7   | 16000  | 0.0538          | 0.0083 |
| 0.0944        | 2.78  | 16500  | 0.0471          | 0.0078 |
| 0.0896        | 2.87  | 17000  | 0.0417          | 0.0064 |
| 0.0895        | 2.95  | 17500  | 0.0406          | 0.0063 |
| 0.0866        | 3.03  | 18000  | 0.0393          | 0.0064 |
| 0.0795        | 3.12  | 18500  | 0.0377          | 0.0067 |
| 0.0803        | 3.2   | 19000  | 0.0380          | 0.0063 |
| 0.0771        | 3.29  | 19500  | 0.0386          | 0.0058 |
| 0.0742        | 3.37  | 20000  | 0.0347          | 0.0057 |
| 0.0743        | 3.46  | 20500  | 0.0363          | 0.0062 |
| 0.073         | 3.54  | 21000  | 0.0314          | 0.0051 |
| 0.0727        | 3.62  | 21500  | 0.0293          | 0.0050 |
| 0.0693        | 3.71  | 22000  | 0.0331          | 0.0057 |
| 0.0689        | 3.79  | 22500  | 0.0306          | 0.0050 |
| 0.0712        | 3.88  | 23000  | 0.0304          | 0.0049 |
| 0.0697        | 3.96  | 23500  | 0.0287          | 0.0050 |
| 0.0656        | 4.05  | 24000  | 0.0269          | 0.0046 |
| 0.0624        | 4.13  | 24500  | 0.0282          | 0.0048 |
| 0.0614        | 4.21  | 25000  | 0.0262          | 0.0046 |
| 0.0589        | 4.3   | 25500  | 0.0245          | 0.0044 |
| 0.0596        | 4.38  | 26000  | 0.0237          | 0.0043 |
| 0.0589        | 4.47  | 26500  | 0.0233          | 0.0045 |
| 0.059         | 4.55  | 27000  | 0.0275          | 0.0044 |
| 0.0564        | 4.64  | 27500  | 0.0241          | 0.0042 |
| 0.0589        | 4.72  | 28000  | 0.0209          | 0.0044 |
| 0.055         | 4.8   | 28500  | 0.0199          | 0.0039 |
| 0.0629        | 4.89  | 29000  | 0.0223          | 0.0046 |
| 0.0581        | 4.97  | 29500  | 0.0234          | 0.0044 |
| 0.0529        | 5.06  | 30000  | 0.0197          | 0.0037 |
| 0.0489        | 5.14  | 30500  | 0.0232          | 0.0044 |
| 0.0512        | 5.23  | 31000  | 0.0202          | 0.0039 |
| 0.0501        | 5.31  | 31500  | 0.0182          | 0.0036 |
| 0.0494        | 5.39  | 32000  | 0.0181          | 0.0034 |
| 0.049         | 5.48  | 32500  | 0.0173          | 0.0034 |
| 0.0482        | 5.56  | 33000  | 0.0173          | 0.0034 |
| 0.0505        | 5.65  | 33500  | 0.0172          | 0.0033 |
| 0.048         | 5.73  | 34000  | 0.0168          | 0.0034 |
| 0.0485        | 5.82  | 34500  | 0.0162          | 0.0032 |
| 0.0461        | 5.9   | 35000  | 0.0154          | 0.0032 |
| 0.0436        | 5.98  | 35500  | 0.0152          | 0.0033 |
| 0.043         | 6.07  | 36000  | 0.0157          | 0.0033 |
| 0.0429        | 6.15  | 36500  | 0.0160          | 0.0036 |
| 0.0402        | 6.24  | 37000  | 0.0169          | 0.0034 |
| 0.043         | 6.32  | 37500  | 0.0135          | 0.0030 |
| 0.0409        | 6.41  | 38000  | 0.0167          | 0.0033 |
| 0.0446        | 6.49  | 38500  | 0.0155          | 0.0035 |
| 0.0402        | 6.57  | 39000  | 0.0145          | 0.0029 |
| 0.0427        | 6.66  | 39500  | 0.0139          | 0.0028 |
| 0.0392        | 6.74  | 40000  | 0.0148          | 0.0028 |
| 0.0395        | 6.83  | 40500  | 0.0144          | 0.0026 |
| 0.0396        | 6.91  | 41000  | 0.0208          | 0.0035 |
| 0.0394        | 7.0   | 41500  | 0.0127          | 0.0026 |
| 0.0367        | 7.08  | 42000  | 0.0129          | 0.0023 |
| 0.0368        | 7.16  | 42500  | 0.0120          | 0.0024 |
| 0.0345        | 7.25  | 43000  | 0.0119          | 0.0027 |
| 0.0342        | 7.33  | 43500  | 0.0123          | 0.0026 |
| 0.0368        | 7.42  | 44000  | 0.0109          | 0.0024 |
| 0.0358        | 7.5   | 44500  | 0.0120          | 0.0026 |
| 0.036         | 7.59  | 45000  | 0.0110          | 0.0026 |
| 0.0332        | 7.67  | 45500  | 0.0115          | 0.0024 |
| 0.0356        | 7.75  | 46000  | 0.0128          | 0.0024 |
| 0.0353        | 7.84  | 46500  | 0.0123          | 0.0024 |
| 0.0365        | 7.92  | 47000  | 0.0106          | 0.0023 |
| 0.0341        | 8.01  | 47500  | 0.0110          | 0.0023 |
| 0.0286        | 8.09  | 48000  | 0.0109          | 0.0023 |
| 0.0324        | 8.18  | 48500  | 0.0099          | 0.0022 |
| 0.0321        | 8.26  | 49000  | 0.0111          | 0.0023 |
| 0.0333        | 8.34  | 49500  | 0.0110          | 0.0023 |
| 0.0302        | 8.43  | 50000  | 0.0116          | 0.0026 |
| 0.0325        | 8.51  | 50500  | 0.0123          | 0.0025 |
| 0.0317        | 8.6   | 51000  | 0.0101          | 0.0027 |
| 0.032         | 8.68  | 51500  | 0.0108          | 0.0024 |
| 0.0298        | 8.77  | 52000  | 0.0105          | 0.0024 |
| 0.0304        | 8.85  | 52500  | 0.0113          | 0.0022 |
| 0.0315        | 8.93  | 53000  | 0.0109          | 0.0023 |
| 0.0299        | 9.02  | 53500  | 0.0102          | 0.0022 |
| 0.029         | 9.1   | 54000  | 0.0108          | 0.0021 |
| 0.0261        | 9.19  | 54500  | 0.0112          | 0.0022 |
| 0.0286        | 9.27  | 55000  | 0.0116          | 0.0023 |
| 0.0293        | 9.36  | 55500  | 0.0108          | 0.0023 |
| 0.0267        | 9.44  | 56000  | 0.0109          | 0.0023 |
| 0.0271        | 9.52  | 56500  | 0.0103          | 0.0021 |
| 0.0276        | 9.61  | 57000  | 0.0102          | 0.0021 |
| 0.0256        | 9.69  | 57500  | 0.0101          | 0.0021 |
| 0.0264        | 9.78  | 58000  | 0.0131          | 0.0021 |
| 0.0277        | 9.86  | 58500  | 0.0096          | 0.0021 |
| 0.0303        | 9.95  | 59000  | 0.0097          | 0.0020 |
| 0.027         | 10.03 | 59500  | 0.0102          | 0.0021 |
| 0.0241        | 10.11 | 60000  | 0.0089          | 0.0020 |
| 0.0226        | 10.2  | 60500  | 0.0108          | 0.0020 |
| 0.0248        | 10.28 | 61000  | 0.0109          | 0.0021 |
| 0.0244        | 10.37 | 61500  | 0.0093          | 0.0020 |
| 0.025         | 10.45 | 62000  | 0.0167          | 0.0021 |
| 0.0238        | 10.54 | 62500  | 0.0123          | 0.0019 |
| 0.0253        | 10.62 | 63000  | 0.0089          | 0.0019 |
| 0.0247        | 10.7  | 63500  | 0.0090          | 0.0019 |
| 0.0229        | 10.79 | 64000  | 0.0084          | 0.0017 |
| 0.0242        | 10.87 | 64500  | 0.0086          | 0.0019 |
| 0.0251        | 10.96 | 65000  | 0.0082          | 0.0017 |
| 0.0222        | 11.04 | 65500  | 0.0081          | 0.0017 |
| 0.0233        | 11.13 | 66000  | 0.0078          | 0.0018 |
| 0.0226        | 11.21 | 66500  | 0.0081          | 0.0017 |
| 0.0227        | 11.29 | 67000  | 0.0096          | 0.0018 |
| 0.024         | 11.38 | 67500  | 0.0081          | 0.0017 |
| 0.0217        | 11.46 | 68000  | 0.0088          | 0.0019 |
| 0.0222        | 11.55 | 68500  | 0.0086          | 0.0019 |
| 0.0229        | 11.63 | 69000  | 0.0091          | 0.0021 |
| 0.02          | 11.72 | 69500  | 0.0094          | 0.0020 |
| 0.022         | 11.8  | 70000  | 0.0095          | 0.0017 |
| 0.0232        | 11.88 | 70500  | 0.0078          | 0.0018 |
| 0.0216        | 11.97 | 71000  | 0.0088          | 0.0017 |
| 0.0216        | 12.05 | 71500  | 0.0081          | 0.0017 |
| 0.0182        | 12.14 | 72000  | 0.0088          | 0.0018 |
| 0.0196        | 12.22 | 72500  | 0.0092          | 0.0017 |
| 0.0203        | 12.31 | 73000  | 0.0083          | 0.0018 |
| 0.0191        | 12.39 | 73500  | 0.0086          | 0.0018 |
| 0.0192        | 12.47 | 74000  | 0.0122          | 0.0016 |
| 0.0194        | 12.56 | 74500  | 0.0084          | 0.0016 |
| 0.0192        | 12.64 | 75000  | 0.0098          | 0.0016 |
| 0.0192        | 12.73 | 75500  | 0.0101          | 0.0017 |
| 0.0191        | 12.81 | 76000  | 0.0100          | 0.0016 |
| 0.0191        | 12.9  | 76500  | 0.0091          | 0.0016 |
| 0.0184        | 12.98 | 77000  | 0.0084          | 0.0017 |
| 0.0188        | 13.06 | 77500  | 0.0080          | 0.0016 |
| 0.0188        | 13.15 | 78000  | 0.0094          | 0.0017 |
| 0.0172        | 13.23 | 78500  | 0.0098          | 0.0017 |
| 0.0174        | 13.32 | 79000  | 0.0134          | 0.0015 |
| 0.0177        | 13.4  | 79500  | 0.0106          | 0.0016 |
| 0.0178        | 13.49 | 80000  | 0.0100          | 0.0014 |
| 0.017         | 13.57 | 80500  | 0.0104          | 0.0016 |
| 0.0169        | 13.65 | 81000  | 0.0094          | 0.0014 |
| 0.0189        | 13.74 | 81500  | 0.0089          | 0.0015 |
| 0.0172        | 13.82 | 82000  | 0.0086          | 0.0016 |
| 0.0167        | 13.91 | 82500  | 0.0091          | 0.0015 |
| 0.0179        | 13.99 | 83000  | 0.0088          | 0.0015 |
| 0.0175        | 14.08 | 83500  | 0.0076          | 0.0014 |
| 0.0164        | 14.16 | 84000  | 0.0082          | 0.0013 |
| 0.0143        | 14.24 | 84500  | 0.0080          | 0.0015 |
| 0.0158        | 14.33 | 85000  | 0.0082          | 0.0014 |
| 0.0153        | 14.41 | 85500  | 0.0086          | 0.0016 |
| 0.0173        | 14.5  | 86000  | 0.0077          | 0.0015 |
| 0.0149        | 14.58 | 86500  | 0.0084          | 0.0016 |
| 0.0153        | 14.67 | 87000  | 0.0078          | 0.0015 |
| 0.0157        | 14.75 | 87500  | 0.0074          | 0.0014 |
| 0.0173        | 14.83 | 88000  | 0.0086          | 0.0015 |
| 0.0171        | 14.92 | 88500  | 0.0080          | 0.0014 |
| 0.0148        | 15.0  | 89000  | 0.0073          | 0.0013 |
| 0.0145        | 15.09 | 89500  | 0.0074          | 0.0014 |
| 0.0153        | 15.17 | 90000  | 0.0068          | 0.0014 |
| 0.0156        | 15.26 | 90500  | 0.0070          | 0.0014 |
| 0.0143        | 15.34 | 91000  | 0.0072          | 0.0014 |
| 0.0143        | 15.42 | 91500  | 0.0069          | 0.0015 |
| 0.0139        | 15.51 | 92000  | 0.0070          | 0.0014 |
| 0.0149        | 15.59 | 92500  | 0.0087          | 0.0015 |
| 0.0124        | 15.68 | 93000  | 0.0074          | 0.0013 |
| 0.0139        | 15.76 | 93500  | 0.0076          | 0.0013 |
| 0.0148        | 15.85 | 94000  | 0.0074          | 0.0014 |
| 0.013         | 15.93 | 94500  | 0.0073          | 0.0014 |
| 0.0138        | 16.01 | 95000  | 0.0069          | 0.0013 |
| 0.0148        | 16.1  | 95500  | 0.0069          | 0.0013 |
| 0.0135        | 16.18 | 96000  | 0.0067          | 0.0014 |
| 0.0143        | 16.27 | 96500  | 0.0067          | 0.0012 |
| 0.014         | 16.35 | 97000  | 0.0071          | 0.0013 |
| 0.0138        | 16.44 | 97500  | 0.0072          | 0.0012 |
| 0.0132        | 16.52 | 98000  | 0.0070          | 0.0012 |
| 0.0122        | 16.6  | 98500  | 0.0077          | 0.0013 |
| 0.0124        | 16.69 | 99000  | 0.0078          | 0.0013 |
| 0.0138        | 16.77 | 99500  | 0.0070          | 0.0012 |
| 0.0118        | 16.86 | 100000 | 0.0071          | 0.0013 |
| 0.0123        | 16.94 | 100500 | 0.0068          | 0.0013 |
| 0.0113        | 17.03 | 101000 | 0.0070          | 0.0013 |
| 0.011         | 17.11 | 101500 | 0.0076          | 0.0013 |
| 0.0117        | 17.19 | 102000 | 0.0073          | 0.0013 |
| 0.0123        | 17.28 | 102500 | 0.0078          | 0.0012 |
| 0.0132        | 17.36 | 103000 | 0.0072          | 0.0013 |
| 0.0105        | 17.45 | 103500 | 0.0078          | 0.0012 |
| 0.0115        | 17.53 | 104000 | 0.0078          | 0.0012 |
| 0.0112        | 17.62 | 104500 | 0.0076          | 0.0012 |
| 0.0108        | 17.7  | 105000 | 0.0080          | 0.0012 |
| 0.0106        | 17.78 | 105500 | 0.0080          | 0.0012 |
| 0.0114        | 17.87 | 106000 | 0.0074          | 0.0012 |
| 0.0127        | 17.95 | 106500 | 0.0070          | 0.0012 |
| 0.0105        | 18.04 | 107000 | 0.0073          | 0.0012 |
| 0.0108        | 18.12 | 107500 | 0.0083          | 0.0012 |
| 0.0107        | 18.21 | 108000 | 0.0088          | 0.0012 |
| 0.0102        | 18.29 | 108500 | 0.0080          | 0.0011 |
| 0.0089        | 18.37 | 109000 | 0.0073          | 0.0011 |
| 0.0107        | 18.46 | 109500 | 0.0073          | 0.0011 |
| 0.0112        | 18.54 | 110000 | 0.0078          | 0.0011 |
| 0.0106        | 18.63 | 110500 | 0.0077          | 0.0011 |
| 0.0101        | 18.71 | 111000 | 0.0078          | 0.0011 |
| 0.0111        | 18.8  | 111500 | 0.0080          | 0.0011 |
| 0.0106        | 18.88 | 112000 | 0.0073          | 0.0011 |
| 0.0102        | 18.96 | 112500 | 0.0075          | 0.0011 |
| 0.0098        | 19.05 | 113000 | 0.0075          | 0.0011 |
| 0.0118        | 19.13 | 113500 | 0.0078          | 0.0011 |
| 0.0094        | 19.22 | 114000 | 0.0077          | 0.0011 |
| 0.009         | 19.3  | 114500 | 0.0077          | 0.0011 |
| 0.0094        | 19.39 | 115000 | 0.0077          | 0.0011 |
| 0.0084        | 19.47 | 115500 | 0.0074          | 0.0010 |
| 0.011         | 19.55 | 116000 | 0.0076          | 0.0011 |
| 0.0106        | 19.64 | 116500 | 0.0073          | 0.0011 |
| 0.009         | 19.72 | 117000 | 0.0074          | 0.0011 |
| 0.0097        | 19.81 | 117500 | 0.0074          | 0.0011 |
| 0.0095        | 19.89 | 118000 | 0.0074          | 0.0011 |
| 0.0094        | 19.98 | 118500 | 0.0074          | 0.0011 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "ft_1", "results": []}]}
transformers pytorch tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 

ft\_1
=====


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0074
* Cer: 0.0011


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.13.0
* Tokenizers 0.15.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.13.0
* Tokenizers 0.15.0
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.13.0
* Tokenizers 0.15.0