---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: AMI SDM
---

# Distil Whisper: AMI SDM 

This is a variant of the [AMI SDM](https://huggingface.co/datasets/edinburghstr/ami) dataset, augmented to return the pseudo-labelled Whisper 
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by 
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original 
[dataset card](https://huggingface.co/datasets/edinburghstr/ami).

## Standalone Usage

First, install the latest version of the 🤗 Datasets package:

```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```

The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) 
function:

```python
from datasets import load_dataset

dataset = load_dataset("distil-whisper/ami-sdm", "sdm")
# take the first sample of the validation set
sample = dataset["validation"][0]
```

It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire 
dataset to disk:

```python
from datasets import load_dataset

dataset = load_dataset("distil-whisper/ami-sdm", "sdm", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```

## Distil Whisper Usage

To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the 
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).

## License

This dataset is licensed under cc-by-4.0.