transformers safetensors whisper_for_slu generated_from_trainer dataset:facebook/voxpopuli base_model:qmeeus/whisper-small-multilingual-spoken-ner-pipeline-step-1 model-index endpoints_compatible region:us
---
base_model: qmeeus/whisper-small-multilingual-spoken-ner-pipeline-step-1 
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
metrics:
- wer
model-index:
- name: WhisperForSpokenNER
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: facebook/voxpopuli de+es+fr+nl
      type: facebook/voxpopuli
      config: de+es+fr+nl
      split: train
    metrics:
    - name: Wer
      type: wer
      value: 0.10856103413576902
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# WhisperForSpokenNER

This model is a fine-tuned version of [/esat/audioslave/qmeeus/exp/whisper_slu/train/whisper-small-spoken-ner](https://huggingface.co//esat/audioslave/qmeeus/exp/whisper_slu/train/whisper-small-spoken-ner) on the facebook/voxpopuli de+es+fr+nl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0444
- F1 Score: 0.6098
- Label F1: 0.8369
- Wer: 0.1086

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Score | Label F1 | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 0.0433        | 0.36  | 200  | 0.0523          | 0.6251   | 0.8320   | 0.1043 |
| 0.0391        | 0.71  | 400  | 0.0504          | 0.6207   | 0.8346   | 0.1047 |
| 0.0381        | 1.07  | 600  | 0.0496          | 0.6142   | 0.8322   | 0.1065 |
| 0.0374        | 1.43  | 800  | 0.0484          | 0.6158   | 0.8360   | 0.1071 |
| 0.0374        | 1.79  | 1000 | 0.0474          | 0.6155   | 0.8370   | 0.1069 |
| 0.0342        | 2.14  | 1200 | 0.0474          | 0.6118   | 0.8362   | 0.1077 |
| 0.0362        | 2.5   | 1400 | 0.0468          | 0.6138   | 0.8375   | 0.1079 |
| 0.0351        | 2.86  | 1600 | 0.0461          | 0.6102   | 0.8361   | 0.1082 |
| 0.0339        | 3.22  | 1800 | 0.0466          | 0.6111   | 0.8388   | 0.1079 |
| 0.0323        | 3.57  | 2000 | 0.0467          | 0.6168   | 0.8419   | 0.1088 |
| 0.0338        | 3.93  | 2200 | 0.0457          | 0.6093   | 0.8426   | 0.1086 |
| 0.032         | 4.29  | 2400 | 0.0452          | 0.6090   | 0.8398   | 0.1085 |
| 0.0307        | 4.65  | 2600 | 0.0451          | 0.6139   | 0.8422   | 0.1086 |
| 0.0321        | 5.0   | 2800 | 0.0452          | 0.6116   | 0.8398   | 0.1083 |
| 0.0313        | 5.36  | 3000 | 0.0448          | 0.6116   | 0.8404   | 0.1092 |
| 0.0309        | 5.72  | 3200 | 0.0449          | 0.6109   | 0.8402   | 0.1083 |
| 0.0305        | 6.08  | 3400 | 0.0448          | 0.6086   | 0.8402   | 0.1083 |
| 0.0301        | 6.43  | 3600 | 0.0447          | 0.6116   | 0.8375   | 0.1081 |
| 0.03          | 6.79  | 3800 | 0.0446          | 0.6103   | 0.8401   | 0.1087 |
| 0.0302        | 7.15  | 4000 | 0.0445          | 0.6120   | 0.8388   | 0.1084 |
| 0.0294        | 7.51  | 4200 | 0.0442          | 0.6132   | 0.8396   | 0.1086 |
| 0.03          | 7.86  | 4400 | 0.0444          | 0.6112   | 0.8382   | 0.1088 |
| 0.03          | 8.22  | 4600 | 0.0445          | 0.6109   | 0.8371   | 0.1087 |
| 0.0296        | 8.58  | 4800 | 0.0444          | 0.6117   | 0.8378   | 0.1084 |
| 0.0297        | 8.94  | 5000 | 0.0444          | 0.6098   | 0.8369   | 0.1086 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
