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cdactvm
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w2v-bert-2.0-hindi_v1

Automatic Speech Recognition
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wav2vec2-bert
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README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Direct Use
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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+ license: mit
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+ base_model: facebook/w2v-bert-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: w2v-bert-2.0-hindi_v1
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+ results: []
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # w2v-bert-2.0-hindi_v1
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+
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+ This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0787
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+ - Wer: 0.0505
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3.5356e-05
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 4
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 2
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:------:|:-----:|:---------------:|:------:|
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+ | 4.508 | 0.0108 | 300 | 3.5169 | 1.0 |
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+ | 2.447 | 0.0216 | 600 | 1.1256 | 0.7027 |
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+ | 1.2978 | 0.0324 | 900 | 0.7873 | 0.4987 |
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+ | 1.034 | 0.0432 | 1200 | 0.6345 | 0.4258 |
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+ | 0.9139 | 0.0540 | 1500 | 0.5973 | 0.3962 |
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+ | 0.8422 | 0.0648 | 1800 | 0.5562 | 0.3586 |
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+ | 0.7939 | 0.0755 | 2100 | 0.4826 | 0.3295 |
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+ | 0.7194 | 0.0863 | 2400 | 0.4829 | 0.3266 |
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+ | 0.737 | 0.0971 | 2700 | 0.4913 | 0.3557 |
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+ | 0.6676 | 0.1079 | 3000 | 0.4541 | 0.3187 |
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+ | 0.6265 | 0.1187 | 3300 | 0.4660 | 0.3088 |
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+ | 0.6296 | 0.1295 | 3600 | 0.4080 | 0.2976 |
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+ | 0.5943 | 0.1403 | 3900 | 0.4042 | 0.2799 |
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+ | 0.6052 | 0.1511 | 4200 | 0.4212 | 0.2945 |
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+ | 0.554 | 0.1619 | 4500 | 0.3867 | 0.2707 |
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+ | 0.5613 | 0.1727 | 4800 | 0.3947 | 0.2881 |
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+ | 0.5254 | 0.1835 | 5100 | 0.3586 | 0.2653 |
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+ | 0.5288 | 0.1943 | 5400 | 0.3691 | 0.2801 |
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+ | 0.5152 | 0.2051 | 5700 | 0.3619 | 0.2555 |
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+ | 0.5361 | 0.2158 | 6000 | 0.3288 | 0.2401 |
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+ | 0.5086 | 0.2266 | 6300 | 0.3216 | 0.2415 |
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+ | 0.4799 | 0.2374 | 6600 | 0.3366 | 0.2467 |
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+ | 0.4876 | 0.2482 | 6900 | 0.3282 | 0.2460 |
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+ | 0.5001 | 0.2590 | 7200 | 0.3300 | 0.2499 |
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+ | 0.4737 | 0.2698 | 7500 | 0.3494 | 0.2385 |
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+ | 0.4768 | 0.2806 | 7800 | 0.3058 | 0.2368 |
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+ | 0.435 | 0.2914 | 8100 | 0.3623 | 0.2561 |
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+ | 0.4366 | 0.3022 | 8400 | 0.3111 | 0.2359 |
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+ | 0.4155 | 0.3130 | 8700 | 0.2987 | 0.2348 |
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+ | 0.4104 | 0.3238 | 9000 | 0.2932 | 0.2312 |
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+ | 0.406 | 0.3346 | 9300 | 0.3100 | 0.2173 |
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+ | 0.397 | 0.3454 | 9600 | 0.2972 | 0.2204 |
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+ | 0.4224 | 0.3561 | 9900 | 0.3044 | 0.2212 |
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+ | 0.3851 | 0.3669 | 10200 | 0.2941 | 0.2165 |
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+ | 0.3684 | 0.3777 | 10500 | 0.2742 | 0.2084 |
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+ | 0.3884 | 0.3885 | 10800 | 0.2633 | 0.2122 |
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+ | 0.3681 | 0.3993 | 11100 | 0.2799 | 0.2089 |
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+ | 0.3468 | 0.4101 | 11400 | 0.2873 | 0.2080 |
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+ | 0.3753 | 0.4209 | 11700 | 0.2533 | 0.1978 |
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+ | 0.3837 | 0.4317 | 12000 | 0.2628 | 0.2054 |
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+ | 0.3442 | 0.4425 | 12300 | 0.2609 | 0.1994 |
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+ | 0.3338 | 0.4533 | 12600 | 0.2512 | 0.2001 |
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+ | 0.3593 | 0.4641 | 12900 | 0.2472 | 0.1954 |
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+ | 0.3311 | 0.4749 | 13200 | 0.2705 | 0.1929 |
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+ | 0.329 | 0.4857 | 13500 | 0.2545 | 0.1997 |
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+ | 0.3122 | 0.4964 | 13800 | 0.2489 | 0.1931 |
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+ | 0.3368 | 0.5072 | 14100 | 0.2568 | 0.1924 |
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+ | 0.3364 | 0.5180 | 14400 | 0.2447 | 0.1949 |
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+ | 0.367 | 0.5288 | 14700 | 0.2325 | 0.1849 |
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+ | 0.3253 | 0.5396 | 15000 | 0.2448 | 0.1839 |
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+ | 0.3166 | 0.5504 | 15300 | 0.2421 | 0.1902 |
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+ | 0.3232 | 0.5612 | 15600 | 0.2319 | 0.1833 |
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+ | 0.2959 | 0.5720 | 15900 | 0.2333 | 0.1757 |
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+ | 0.315 | 0.5828 | 16200 | 0.2372 | 0.1809 |
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+ | 0.2854 | 0.5936 | 16500 | 0.2400 | 0.1810 |
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+ | 0.3361 | 0.6044 | 16800 | 0.2573 | 0.1780 |
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+ | 0.3027 | 0.6152 | 17100 | 0.2308 | 0.1744 |
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+ | 0.3015 | 0.6259 | 17400 | 0.2405 | 0.1736 |
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+ | 0.3035 | 0.6367 | 17700 | 0.2322 | 0.1822 |
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+ | 0.2882 | 0.6475 | 18000 | 0.2297 | 0.1762 |
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+ | 0.267 | 0.6583 | 18300 | 0.2155 | 0.1652 |
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+ | 0.2819 | 0.6691 | 18600 | 0.2156 | 0.1612 |
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+ | 0.2898 | 0.6799 | 18900 | 0.2116 | 0.1585 |
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+ | 0.2857 | 0.6907 | 19200 | 0.1987 | 0.1531 |
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+ | 0.2826 | 0.7015 | 19500 | 0.1909 | 0.1556 |
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+ | 0.2774 | 0.7123 | 19800 | 0.1858 | 0.1499 |
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+ | 0.293 | 0.7231 | 20100 | 0.1940 | 0.1503 |
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+ | 0.2771 | 0.7339 | 20400 | 0.1994 | 0.1521 |
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+ | 0.2664 | 0.7447 | 20700 | 0.1948 | 0.1519 |
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+ | 0.261 | 0.7555 | 21000 | 0.1875 | 0.1442 |
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+ | 0.2467 | 0.7662 | 21300 | 0.1887 | 0.1439 |
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+ | 0.2435 | 0.7770 | 21600 | 0.2039 | 0.1452 |
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+ | 0.2459 | 0.7878 | 21900 | 0.1825 | 0.1398 |
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+ | 0.2367 | 0.7986 | 22200 | 0.2007 | 0.1439 |
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+ | 0.2383 | 0.8094 | 22500 | 0.1901 | 0.1419 |
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+ | 0.2524 | 0.8202 | 22800 | 0.1727 | 0.1409 |
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+ | 0.248 | 0.8310 | 23100 | 0.1926 | 0.1405 |
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+ | 0.265 | 0.8418 | 23400 | 0.1795 | 0.1353 |
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+ | 0.2469 | 0.8526 | 23700 | 0.1712 | 0.1301 |
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+ | 0.2212 | 0.8634 | 24000 | 0.1841 | 0.1389 |
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+ | 0.2591 | 0.8742 | 24300 | 0.1783 | 0.1281 |
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+ | 0.2311 | 0.8850 | 24600 | 0.1843 | 0.1342 |
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+ | 0.2297 | 0.8958 | 24900 | 0.1652 | 0.1326 |
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+ | 0.2203 | 0.9065 | 25200 | 0.1608 | 0.1263 |
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+ | 0.222 | 0.9173 | 25500 | 0.1788 | 0.1267 |
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+ | 0.2232 | 0.9281 | 25800 | 0.1614 | 0.1226 |
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+ | 0.2165 | 0.9389 | 26100 | 0.1746 | 0.1231 |
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+ | 0.2111 | 0.9497 | 26400 | 0.1793 | 0.1274 |
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+ | 0.2344 | 0.9605 | 26700 | 0.1645 | 0.1209 |
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+ | 0.2075 | 0.9713 | 27000 | 0.1609 | 0.1243 |
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+ | 0.212 | 0.9821 | 27300 | 0.1750 | 0.1294 |
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+ | 0.1863 | 0.9929 | 27600 | 0.1595 | 0.1179 |
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+ | 0.1876 | 1.0037 | 27900 | 0.1535 | 0.1150 |
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+ | 0.1708 | 1.0145 | 28200 | 0.1599 | 0.1159 |
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+ | 0.1624 | 1.0253 | 28500 | 0.1587 | 0.1172 |
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+ | 0.1837 | 1.0361 | 28800 | 0.1561 | 0.1160 |
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+ | 0.1894 | 1.0468 | 29100 | 0.1593 | 0.1079 |
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+ | 0.1656 | 1.0576 | 29400 | 0.1549 | 0.1115 |
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+ | 0.1809 | 1.0684 | 29700 | 0.1333 | 0.1093 |
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+ | 0.1814 | 1.0792 | 30000 | 0.1458 | 0.1058 |
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+ | 0.159 | 1.0900 | 30300 | 0.1460 | 0.1091 |
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+ | 0.1707 | 1.1008 | 30600 | 0.1430 | 0.1077 |
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+ | 0.1728 | 1.1116 | 30900 | 0.1564 | 0.1026 |
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+ | 0.1583 | 1.1224 | 31200 | 0.1408 | 0.1021 |
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+ | 0.1751 | 1.1332 | 31500 | 0.1464 | 0.1048 |
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+ | 0.1686 | 1.1440 | 31800 | 0.1371 | 0.0999 |
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+ | 0.1495 | 1.1548 | 32100 | 0.1448 | 0.0996 |
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+ | 0.1647 | 1.1656 | 32400 | 0.1452 | 0.1004 |
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+ | 0.151 | 1.1764 | 32700 | 0.1376 | 0.0993 |
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+ | 0.1507 | 1.1871 | 33000 | 0.1308 | 0.0947 |
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+ | 0.154 | 1.1979 | 33300 | 0.1315 | 0.0975 |
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+ | 0.1452 | 1.2087 | 33600 | 0.1281 | 0.0951 |
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+ | 0.1381 | 1.2195 | 33900 | 0.1329 | 0.0936 |
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+ | 0.146 | 1.2303 | 34200 | 0.1304 | 0.0905 |
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+ | 0.1697 | 1.2411 | 34500 | 0.1265 | 0.0930 |
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+ | 0.1479 | 1.2519 | 34800 | 0.1245 | 0.0896 |
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+ | 0.1583 | 1.2627 | 35100 | 0.1292 | 0.0888 |
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+ | 0.1246 | 1.2735 | 35400 | 0.1330 | 0.0939 |
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+ | 0.1537 | 1.2843 | 35700 | 0.1279 | 0.0865 |
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+ | 0.142 | 1.2951 | 36000 | 0.1221 | 0.0877 |
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+ | 0.1312 | 1.3059 | 36300 | 0.1222 | 0.0876 |
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+ | 0.1364 | 1.3167 | 36600 | 0.1235 | 0.0881 |
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+ | 0.1527 | 1.3274 | 36900 | 0.1241 | 0.0834 |
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+ | 0.1362 | 1.3382 | 37200 | 0.1177 | 0.0810 |
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+ | 0.1546 | 1.3490 | 37500 | 0.1212 | 0.0801 |
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+ | 0.1341 | 1.3598 | 37800 | 0.1231 | 0.0819 |
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+ | 0.1371 | 1.3706 | 38100 | 0.1196 | 0.0865 |
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+ | 0.1425 | 1.3814 | 38400 | 0.1126 | 0.0805 |
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+ | 0.16 | 1.3922 | 38700 | 0.1185 | 0.0783 |
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+ | 0.1316 | 1.4030 | 39000 | 0.1204 | 0.0794 |
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+ | 0.1361 | 1.4138 | 39300 | 0.1091 | 0.0777 |
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+ | 0.1623 | 1.4246 | 39600 | 0.1090 | 0.0776 |
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+ | 0.1246 | 1.4354 | 39900 | 0.1115 | 0.0779 |
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+ | 0.1289 | 1.4462 | 40200 | 0.1081 | 0.0748 |
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+ | 0.1124 | 1.4570 | 40500 | 0.1083 | 0.0745 |
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+ | 0.1224 | 1.4677 | 40800 | 0.1072 | 0.0755 |
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+ | 0.1218 | 1.4785 | 41100 | 0.1132 | 0.0739 |
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+ | 0.121 | 1.4893 | 41400 | 0.1085 | 0.0733 |
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+ | 0.1058 | 1.5001 | 41700 | 0.1098 | 0.0720 |
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+ | 0.1304 | 1.5109 | 42000 | 0.1044 | 0.0694 |
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+ | 0.1309 | 1.5217 | 42300 | 0.1045 | 0.0694 |
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+ | 0.1418 | 1.5325 | 42600 | 0.0997 | 0.0675 |
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+ | 0.1213 | 1.5433 | 42900 | 0.1039 | 0.0698 |
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+ | 0.1253 | 1.5541 | 43200 | 0.1024 | 0.0695 |
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+ | 0.1119 | 1.5649 | 43500 | 0.1043 | 0.0706 |
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+ | 0.1132 | 1.5757 | 43800 | 0.1043 | 0.0665 |
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+ | 0.1161 | 1.5865 | 44100 | 0.1041 | 0.0644 |
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+ | 0.095 | 1.5973 | 44400 | 0.1014 | 0.0656 |
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+ | 0.0958 | 1.6080 | 44700 | 0.0972 | 0.0640 |
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+ | 0.1035 | 1.6188 | 45000 | 0.1003 | 0.0652 |
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+ | 0.1054 | 1.6296 | 45300 | 0.1043 | 0.0666 |
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+ | 0.1172 | 1.6404 | 45600 | 0.1002 | 0.0643 |
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+ | 0.1078 | 1.6512 | 45900 | 0.0996 | 0.0641 |
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+ | 0.102 | 1.6620 | 46200 | 0.0973 | 0.0619 |
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+ | 0.108 | 1.6728 | 46500 | 0.0966 | 0.0609 |
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+ | 0.1058 | 1.6836 | 46800 | 0.0938 | 0.0613 |
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+ | 0.1134 | 1.6944 | 47100 | 0.0905 | 0.0606 |
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+ | 0.1102 | 1.7052 | 47400 | 0.0915 | 0.0598 |
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+ | 0.1342 | 1.7160 | 47700 | 0.0903 | 0.0587 |
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+ | 0.1039 | 1.7268 | 48000 | 0.0905 | 0.0590 |
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+ | 0.0993 | 1.7376 | 48300 | 0.0924 | 0.0596 |
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+ | 0.0965 | 1.7483 | 48600 | 0.0898 | 0.0580 |
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+ | 0.0911 | 1.7591 | 48900 | 0.0899 | 0.0577 |
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+ | 0.1023 | 1.7699 | 49200 | 0.0897 | 0.0577 |
220
+ | 0.094 | 1.7807 | 49500 | 0.0875 | 0.0558 |
221
+ | 0.0962 | 1.7915 | 49800 | 0.0880 | 0.0558 |
222
+ | 0.0922 | 1.8023 | 50100 | 0.0858 | 0.0555 |
223
+ | 0.0945 | 1.8131 | 50400 | 0.0866 | 0.0548 |
224
+ | 0.0897 | 1.8239 | 50700 | 0.0840 | 0.0542 |
225
+ | 0.0921 | 1.8347 | 51000 | 0.0876 | 0.0549 |
226
+ | 0.0917 | 1.8455 | 51300 | 0.0853 | 0.0540 |
227
+ | 0.1093 | 1.8563 | 51600 | 0.0844 | 0.0540 |
228
+ | 0.0986 | 1.8671 | 51900 | 0.0831 | 0.0536 |
229
+ | 0.0904 | 1.8778 | 52200 | 0.0831 | 0.0530 |
230
+ | 0.096 | 1.8886 | 52500 | 0.0825 | 0.0531 |
231
+ | 0.0815 | 1.8994 | 52800 | 0.0837 | 0.0533 |
232
+ | 0.0892 | 1.9102 | 53100 | 0.0840 | 0.0533 |
233
+ | 0.0789 | 1.9210 | 53400 | 0.0826 | 0.0524 |
234
+ | 0.0914 | 1.9318 | 53700 | 0.0813 | 0.0520 |
235
+ | 0.1029 | 1.9426 | 54000 | 0.0803 | 0.0513 |
236
+ | 0.0856 | 1.9534 | 54300 | 0.0798 | 0.0511 |
237
+ | 0.0869 | 1.9642 | 54600 | 0.0794 | 0.0507 |
238
+ | 0.101 | 1.9750 | 54900 | 0.0785 | 0.0508 |
239
+ | 0.0917 | 1.9858 | 55200 | 0.0787 | 0.0507 |
240
+ | 0.0875 | 1.9966 | 55500 | 0.0787 | 0.0505 |
241
+
242
+
243
+ ### Framework versions
244
+
245
+ - Transformers 4.41.1
246
+ - Pytorch 2.1.2+cu121
247
+ - Datasets 2.19.1
248
+ - Tokenizers 0.19.1
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