---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_13_0
language:
- multilingual
library_name: peft
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Small 2 lang LORA 2nd Settings
  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. -->

# Whisper Small 2 lang LORA 2nd Settings

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2569

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.9564        | 0.1002 | 250   | 2.1385          |
| 1.2046        | 0.2004 | 500   | 1.1713          |
| 0.8719        | 0.3006 | 750   | 0.9414          |
| 0.7799        | 0.4008 | 1000  | 0.8261          |
| 0.6956        | 0.5010 | 1250  | 0.6536          |
| 0.3536        | 0.6012 | 1500  | 0.2990          |
| 0.3167        | 0.7014 | 1750  | 0.2902          |
| 0.3252        | 0.8016 | 2000  | 0.2850          |
| 0.3133        | 0.9018 | 2250  | 0.2821          |
| 0.3764        | 1.0020 | 2500  | 0.2800          |
| 0.2942        | 1.1022 | 2750  | 0.2775          |
| 0.2937        | 1.2024 | 3000  | 0.2770          |
| 0.3478        | 1.3026 | 3250  | 0.2745          |
| 0.3776        | 1.4028 | 3500  | 0.2729          |
| 0.3099        | 1.5030 | 3750  | 0.2713          |
| 0.3087        | 1.6032 | 4000  | 0.2705          |
| 0.2998        | 1.7034 | 4250  | 0.2699          |
| 0.3226        | 1.8036 | 4500  | 0.2683          |
| 0.3589        | 1.9038 | 4750  | 0.2676          |
| 0.3411        | 2.0040 | 5000  | 0.2673          |
| 0.3084        | 2.1042 | 5250  | 0.2674          |
| 0.31          | 2.2044 | 5500  | 0.2663          |
| 0.3388        | 2.3046 | 5750  | 0.2657          |
| 0.2716        | 2.4048 | 6000  | 0.2652          |
| 0.3059        | 2.5050 | 6250  | 0.2652          |
| 0.27          | 2.6052 | 6500  | 0.2648          |
| 0.2954        | 2.7054 | 6750  | 0.2639          |
| 0.336         | 2.8056 | 7000  | 0.2641          |
| 0.2833        | 2.9058 | 7250  | 0.2631          |
| 0.2777        | 3.0060 | 7500  | 0.2624          |
| 0.2418        | 3.1062 | 7750  | 0.2618          |
| 0.3194        | 3.2064 | 8000  | 0.2623          |
| 0.3319        | 3.3066 | 8250  | 0.2623          |
| 0.3551        | 3.4068 | 8500  | 0.2615          |
| 0.3421        | 3.5070 | 8750  | 0.2619          |
| 0.3862        | 3.6072 | 9000  | 0.2616          |
| 0.2437        | 3.7074 | 9250  | 0.2609          |
| 0.2995        | 3.8076 | 9500  | 0.2604          |
| 0.3535        | 3.9078 | 9750  | 0.2603          |
| 0.2871        | 4.0080 | 10000 | 0.2601          |
| 0.2908        | 4.1082 | 10250 | 0.2604          |
| 0.3203        | 4.2084 | 10500 | 0.2599          |
| 0.2598        | 4.3086 | 10750 | 0.2594          |
| 0.2942        | 4.4088 | 11000 | 0.2593          |
| 0.3302        | 4.5090 | 11250 | 0.2590          |
| 0.3615        | 4.6092 | 11500 | 0.2584          |
| 0.3291        | 4.7094 | 11750 | 0.2582          |
| 0.2781        | 4.8096 | 12000 | 0.2588          |
| 0.3106        | 4.9098 | 12250 | 0.2585          |
| 0.2484        | 5.0100 | 12500 | 0.2583          |
| 0.2645        | 5.1102 | 12750 | 0.2583          |
| 0.3034        | 5.2104 | 13000 | 0.2581          |
| 0.2865        | 5.3106 | 13250 | 0.2576          |
| 0.3301        | 5.4108 | 13500 | 0.2580          |
| 0.3759        | 5.5110 | 13750 | 0.2579          |
| 0.3318        | 5.6112 | 14000 | 0.2581          |
| 0.2825        | 5.7114 | 14250 | 0.2579          |
| 0.2976        | 5.8116 | 14500 | 0.2578          |
| 0.2976        | 5.9118 | 14750 | 0.2577          |
| 0.3681        | 6.0120 | 15000 | 0.2575          |
| 0.3274        | 6.1122 | 15250 | 0.2575          |
| 0.2948        | 6.2124 | 15500 | 0.2577          |
| 0.2932        | 6.3126 | 15750 | 0.2576          |
| 0.2587        | 6.4128 | 16000 | 0.2578          |
| 0.2564        | 6.5130 | 16250 | 0.2573          |
| 0.2776        | 6.6132 | 16500 | 0.2569          |
| 0.2954        | 6.7134 | 16750 | 0.2569          |
| 0.2891        | 6.8136 | 17000 | 0.2568          |
| 0.2373        | 6.9138 | 17250 | 0.2569          |
| 0.3532        | 7.0140 | 17500 | 0.2569          |
| 0.2676        | 7.1142 | 17750 | 0.2569          |
| 0.2763        | 7.2144 | 18000 | 0.2569          |
| 0.2692        | 7.3146 | 18250 | 0.2571          |
| 0.3198        | 7.4148 | 18500 | 0.2570          |
| 0.2158        | 7.5150 | 18750 | 0.2571          |
| 0.277         | 7.6152 | 19000 | 0.2572          |
| 0.2308        | 7.7154 | 19250 | 0.2572          |
| 0.3166        | 7.8156 | 19500 | 0.2569          |
| 0.3064        | 7.9158 | 19750 | 0.2570          |
| 0.2743        | 8.0160 | 20000 | 0.2569          |


### Framework versions

- PEFT 0.11.2.dev0
- Transformers 4.43.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1