transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer zh dataset:google/fleurs base_model:openai/whisper-tiny license:apache-2.0 endpoints_compatible region:us
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- google/fleurs
model-index:
- name: Whisper Tiny Chinese - Tommy
  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 Tiny Chinese - Tommy

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the google fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0604
- Cer: 686.3631

## 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: 16
- 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: 100
- training_steps: 3500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7861        | 2.18  | 500  | 2.9273          | 541.9488 |
| 2.2443        | 4.37  | 1000 | 2.5364          | 440.6712 |
| 1.9019        | 6.55  | 1500 | 2.3037          | 705.8989 |
| 1.6275        | 8.73  | 2000 | 2.1769          | 746.9195 |
| 1.4541        | 10.92 | 2500 | 2.1022          | 756.6663 |
| 1.3193        | 13.1  | 3000 | 2.0731          | 699.0618 |
| 1.2989        | 15.28 | 3500 | 2.0604          | 686.3631 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
