peft safetensors whisper_for_slu generated_from_trainer dataset:facebook/voxpopuli base_model:qmeeus/whisper-small-multilingual-spoken-ner-pipeline-step-1 model-index region:us
---
library_name: peft
tags:
- generated_from_trainer
base_model: qmeeus/whisper-small-multilingual-spoken-ner-pipeline-step-1
datasets:
- facebook/voxpopuli
metrics:
- wer
model-index:
- name: WhisperForSpokenNER
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: facebook/voxpopuli 
      type: facebook/voxpopuli
      split: de+es+fr+nl
    metrics:
    - type: wer
      value: 0.09799520086694016
      name: Wer
---

<!-- 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: 0.2264
- F1 Score: 0.6872
- Label F1: 0.8325
- Wer: 0.0980

## 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: 8
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 0.2746        | 0.36  | 200  | 0.2602          | 0.6565   | 0.8343   | 0.1090 |
| 0.2481        | 0.71  | 400  | 0.2465          | 0.6578   | 0.8348   | 0.1022 |
| 0.2385        | 1.07  | 600  | 0.2410          | 0.6685   | 0.8323   | 0.1048 |
| 0.2316        | 1.43  | 800  | 0.2374          | 0.6724   | 0.8317   | 0.1022 |
| 0.2291        | 1.79  | 1000 | 0.2348          | 0.6698   | 0.8292   | 0.0968 |
| 0.2205        | 2.14  | 1200 | 0.2334          | 0.6745   | 0.8339   | 0.0964 |
| 0.2211        | 2.5   | 1400 | 0.2319          | 0.6729   | 0.8327   | 0.0961 |
| 0.2163        | 2.86  | 1600 | 0.2305          | 0.6732   | 0.8300   | 0.0981 |
| 0.2108        | 3.22  | 1800 | 0.2299          | 0.6734   | 0.8328   | 0.0954 |
| 0.2104        | 3.57  | 2000 | 0.2297          | 0.6792   | 0.8369   | 0.0992 |
| 0.2124        | 3.93  | 2200 | 0.2279          | 0.6782   | 0.8346   | 0.0945 |
| 0.2027        | 4.29  | 2400 | 0.2279          | 0.6790   | 0.8336   | 0.0944 |
| 0.2055        | 4.65  | 2600 | 0.2275          | 0.6832   | 0.8347   | 0.0949 |
| 0.209         | 5.0   | 2800 | 0.2269          | 0.6822   | 0.8336   | 0.0983 |
| 0.2017        | 5.36  | 3000 | 0.2272          | 0.6835   | 0.8346   | 0.0979 |
| 0.2029        | 5.72  | 3200 | 0.2266          | 0.6819   | 0.8321   | 0.0966 |
| 0.201         | 6.08  | 3400 | 0.2266          | 0.6800   | 0.8327   | 0.0978 |
| 0.1985        | 6.43  | 3600 | 0.2267          | 0.6856   | 0.8335   | 0.0995 |
| 0.1996        | 6.79  | 3800 | 0.2265          | 0.6864   | 0.8343   | 0.0988 |
| 0.197         | 7.15  | 4000 | 0.2264          | 0.6843   | 0.8347   | 0.0986 |
| 0.1985        | 7.51  | 4200 | 0.2264          | 0.6838   | 0.8352   | 0.0985 |
| 0.1999        | 7.86  | 4400 | 0.2263          | 0.6861   | 0.8346   | 0.0978 |
| 0.1963        | 8.22  | 4600 | 0.2264          | 0.6864   | 0.8318   | 0.0978 |
| 0.1977        | 8.58  | 4800 | 0.2264          | 0.6875   | 0.8329   | 0.0979 |
| 0.1961        | 8.94  | 5000 | 0.2264          | 0.6872   | 0.8325   | 0.0980 |


### Framework versions

- PEFT 0.7.1.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
