---
license: unknown
datasets:
- PolyAI/minds14
language:
- en
metrics:
- accuracy
- wer
- f1
- bleu
base_model:
- openai/whisper-tiny
pipeline_tag: automatic-speech-recognition

model-index:
- name: whisper-mind14-enUS
  results:
  - task:
      type: ASR
    dataset:
      name: minds-14
      type: enUS
    metrics:
    - name: Accuracy
      type: Accuracy
      value: 62.25
  - task:
      type: ASR
    dataset:
      name: minds-14
      type: enUS
    metrics:
    - name: wer
      type: wer
      value: 0.38%
  - task:
      type: ASR
    dataset:
      name: minds-14
      type: enUS
    metrics:
    - name: f1
      type: f1
      value: 0.6722
  - task:
      type: ASR
    dataset:
      name: minds-14
      type: enUS
    metrics:
    - name: bleu
      type: bleu
      value: 0.0235
---

this model based on whisper-tiny model that trained with minds-14 dataset, only trained in english version : enUS

example of using model to classify intent:

```python
>>> from transformers import pipeline

model_id = "kairaamilanii/whisper-mind14-enUS"

transcriber = pipeline(
    "automatic-speech-recognition",
    model=model_id,
    chunk_length_s=30,
    device="cuda:0" if torch.cuda.is_available() else "cpu",
)

audio_file = "/content/602b9a90963e11ccd901cbd0.wav"  # Replace with your audio file path

text = transcriber(audio_file)
text
```
example output:
```python
{'text': "hello i was looking at my recent transactions and i saw that there's a payment that i didn't make will you be able to stop this thank you"}
```