pytorch tensorboard wav2vec2 automatic-speech-recognition transformers gary109/AI_Light_Dance generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram

This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4693
- Wer: 0.2046

## 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: 4e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3028        | 1.0   | 288   | 0.4693          | 0.2046 |
| 0.2986        | 2.0   | 576   | 0.4828          | 0.2058 |
| 0.297         | 3.0   | 864   | 0.5020          | 0.2038 |
| 0.2863        | 4.0   | 1152  | 0.5216          | 0.2020 |
| 0.3036        | 5.0   | 1440  | 0.4963          | 0.2008 |
| 0.3141        | 6.0   | 1728  | 0.5005          | 0.2020 |
| 0.2898        | 7.0   | 2016  | 0.4962          | 0.2029 |
| 0.2922        | 8.0   | 2304  | 0.5073          | 0.2031 |
| 0.266         | 9.0   | 2592  | 0.5159          | 0.2024 |
| 0.2817        | 10.0  | 2880  | 0.5238          | 0.2011 |
| 0.2922        | 11.0  | 3168  | 0.5080          | 0.2011 |
| 0.2869        | 12.0  | 3456  | 0.4974          | 0.2027 |
| 0.284         | 13.0  | 3744  | 0.5104          | 0.2006 |
| 0.2911        | 14.0  | 4032  | 0.5026          | 0.2017 |
| 0.2864        | 15.0  | 4320  | 0.5065          | 0.2002 |
| 0.2779        | 16.0  | 4608  | 0.5024          | 0.2010 |
| 0.2766        | 17.0  | 4896  | 0.5078          | 0.1998 |
| 0.2872        | 18.0  | 5184  | 0.5114          | 0.1981 |
| 0.268         | 19.0  | 5472  | 0.5078          | 0.1980 |
| 0.2631        | 20.0  | 5760  | 0.5262          | 0.2021 |
| 0.2753        | 21.0  | 6048  | 0.5161          | 0.1991 |
| 0.2797        | 22.0  | 6336  | 0.5097          | 0.2009 |
| 0.2667        | 23.0  | 6624  | 0.5131          | 0.1995 |
| 0.2722        | 24.0  | 6912  | 0.5098          | 0.1990 |
| 0.3026        | 25.0  | 7200  | 0.5193          | 0.2006 |
| 0.2888        | 26.0  | 7488  | 0.4987          | 0.1986 |
| 0.2732        | 27.0  | 7776  | 0.5063          | 0.2007 |
| 0.2567        | 28.0  | 8064  | 0.5103          | 0.2015 |
| 0.2845        | 29.0  | 8352  | 0.5084          | 0.2020 |
| 0.2591        | 30.0  | 8640  | 0.5109          | 0.1989 |
| 0.2777        | 31.0  | 8928  | 0.5179          | 0.1994 |
| 0.2784        | 32.0  | 9216  | 0.5183          | 0.1989 |
| 0.2801        | 33.0  | 9504  | 0.5222          | 0.2003 |
| 0.2554        | 34.0  | 9792  | 0.5137          | 0.1990 |
| 0.2708        | 35.0  | 10080 | 0.5094          | 0.1964 |
| 0.27          | 36.0  | 10368 | 0.5076          | 0.1980 |
| 0.2706        | 37.0  | 10656 | 0.5179          | 0.1983 |
| 0.2791        | 38.0  | 10944 | 0.5154          | 0.1976 |
| 0.3148        | 39.0  | 11232 | 0.5082          | 0.1990 |
| 0.2834        | 40.0  | 11520 | 0.5107          | 0.1980 |
| 0.2739        | 41.0  | 11808 | 0.5009          | 0.1990 |
| 0.2687        | 42.0  | 12096 | 0.5232          | 0.2011 |
| 0.2696        | 43.0  | 12384 | 0.5108          | 0.1986 |
| 0.2729        | 44.0  | 12672 | 0.5159          | 0.1991 |
| 0.2579        | 45.0  | 12960 | 0.5162          | 0.1991 |
| 0.283         | 46.0  | 13248 | 0.5032          | 0.1982 |
| 0.282         | 47.0  | 13536 | 0.5107          | 0.1980 |
| 0.2708        | 48.0  | 13824 | 0.5128          | 0.1982 |
| 0.2562        | 49.0  | 14112 | 0.5163          | 0.1991 |
| 0.2675        | 50.0  | 14400 | 0.5062          | 0.1994 |
| 0.285         | 51.0  | 14688 | 0.4999          | 0.1988 |
| 0.2756        | 52.0  | 14976 | 0.5030          | 0.1986 |
| 0.2888        | 53.0  | 15264 | 0.5043          | 0.1975 |
| 0.2778        | 54.0  | 15552 | 0.5111          | 0.1980 |
| 0.2707        | 55.0  | 15840 | 0.5117          | 0.1995 |
| 0.2566        | 56.0  | 16128 | 0.5197          | 0.2002 |
| 0.2517        | 57.0  | 16416 | 0.5211          | 0.1977 |
| 0.2629        | 58.0  | 16704 | 0.5080          | 0.1986 |
| 0.2787        | 59.0  | 16992 | 0.5133          | 0.1980 |
| 0.269         | 60.0  | 17280 | 0.5156          | 0.1973 |
| 0.2664        | 61.0  | 17568 | 0.5192          | 0.1949 |
| 0.2605        | 62.0  | 17856 | 0.5095          | 0.1970 |
| 0.2649        | 63.0  | 18144 | 0.5149          | 0.1970 |
| 0.246         | 64.0  | 18432 | 0.5165          | 0.1975 |
| 0.2567        | 65.0  | 18720 | 0.5072          | 0.1981 |
| 0.2509        | 66.0  | 19008 | 0.5061          | 0.1978 |
| 0.289         | 67.0  | 19296 | 0.5087          | 0.1957 |
| 0.2511        | 68.0  | 19584 | 0.5168          | 0.1982 |
| 0.2623        | 69.0  | 19872 | 0.5110          | 0.1959 |
| 0.2762        | 70.0  | 20160 | 0.5123          | 0.1959 |
| 0.2704        | 71.0  | 20448 | 0.5118          | 0.1966 |
| 0.2854        | 72.0  | 20736 | 0.5128          | 0.1949 |
| 0.2602        | 73.0  | 21024 | 0.5094          | 0.1966 |
| 0.2675        | 74.0  | 21312 | 0.5058          | 0.1961 |
| 0.2519        | 75.0  | 21600 | 0.5216          | 0.1988 |
| 0.2666        | 76.0  | 21888 | 0.5117          | 0.1959 |
| 0.2637        | 77.0  | 22176 | 0.5058          | 0.1957 |
| 0.273         | 78.0  | 22464 | 0.5187          | 0.1966 |
| 0.2666        | 79.0  | 22752 | 0.5176          | 0.1958 |
| 0.2627        | 80.0  | 23040 | 0.5142          | 0.1950 |
| 0.2508        | 81.0  | 23328 | 0.5158          | 0.1961 |
| 0.2499        | 82.0  | 23616 | 0.5131          | 0.1970 |
| 0.2583        | 83.0  | 23904 | 0.5150          | 0.1975 |
| 0.246         | 84.0  | 24192 | 0.5097          | 0.1962 |
| 0.272         | 85.0  | 24480 | 0.5043          | 0.1950 |
| 0.2601        | 86.0  | 24768 | 0.5091          | 0.1961 |
| 0.2719        | 87.0  | 25056 | 0.5087          | 0.1975 |
| 0.269         | 88.0  | 25344 | 0.5126          | 0.1966 |
| 0.2863        | 89.0  | 25632 | 0.5174          | 0.1966 |
| 0.2581        | 90.0  | 25920 | 0.5159          | 0.1969 |
| 0.26          | 91.0  | 26208 | 0.5146          | 0.1969 |
| 0.2796        | 92.0  | 26496 | 0.5150          | 0.1966 |
| 0.2723        | 93.0  | 26784 | 0.5133          | 0.1971 |
| 0.249         | 94.0  | 27072 | 0.5096          | 0.1961 |
| 0.266         | 95.0  | 27360 | 0.5116          | 0.1964 |
| 0.2683        | 96.0  | 27648 | 0.5133          | 0.1967 |
| 0.2451        | 97.0  | 27936 | 0.5141          | 0.1965 |
| 0.2723        | 98.0  | 28224 | 0.5123          | 0.1962 |
| 0.2527        | 99.0  | 28512 | 0.5120          | 0.1966 |
| 0.2604        | 100.0 | 28800 | 0.5111          | 0.1961 |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
