Instructions to use Xenova/tiny-random-WhisperForConditionalGeneration_timestamped with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/tiny-random-WhisperForConditionalGeneration_timestamped with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'Xenova/tiny-random-WhisperForConditionalGeneration_timestamped');
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Download README.md from Xenova/tiny-random-WhisperForConditionalGeneration_timestamped: direct link, hf CLI and curl.
- Browser
- Download file 831 Bytes
-
https://huggingface.co/Xenova/tiny-random-WhisperForConditionalGeneration_timestamped/resolve/refs%2Fpr%2F2/README.md
- Command line
-
hf download hf://Xenova/tiny-random-WhisperForConditionalGeneration_timestamped@refs/pr/2/README.md
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curl -L -o README.md https://huggingface.co/Xenova/tiny-random-WhisperForConditionalGeneration_timestamped/resolve/refs%2Fpr%2F2/README.md
831 Bytes
metadata
tags:
- transformers.js
Code to generate:
from transformers import WhisperForConditionalGeneration, AutoProcessor
new_config_values = dict(
d_model = 16,
decoder_attention_heads = 4,
decoder_layers = 1,
encoder_attention_heads = 4,
encoder_layers = 1,
num_hidden_layers = 1,
ignore_mismatched_sizes=True,
)
original_model = WhisperForConditionalGeneration.from_pretrained('openai/whisper-tiny', **new_config_values)
original_model.save_pretrained('converted')
original_processor = AutoProcessor.from_pretrained('openai/whisper-tiny')
original_processor.save_pretrained('converted')
Followed by:
$ mkdir -p ./converted/onnx
$ optimum-cli export onnx -m ./converted ./converted/onnx --task automatic-speech-recognition-with-past
$ find ./converted/onnx -type f ! -name "*.onnx" -delete