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May 30, 2024
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docs: fix ambiguity. #724

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docs: fix ambiguity.
Vaibhavs10 May 30, 2024
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apply suggestions from review.
Vaibhavs10 May 30, 2024
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Update packages/tasks/src/tasks/automatic-speech-recognition/about.md
Vaibhavs10 May 30, 2024
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28 changes: 14 additions & 14 deletions packages/tasks/src/tasks/automatic-speech-recognition/about.md
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Expand Up @@ -18,7 +18,7 @@ The use of Multilingual ASR has become popular, the idea of maintaining just a s

## Inference

The Hub contains over [17,000 ASR models](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) that you can test right away in your browser using the model page widgets. You can also use any model as a service using the Inference API. Here is a simple code snippet to do exactly this:
The Hub contains over [17,000 ASR models](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) that you can test right away in your browser using the model page widgets. You can also use any model as a service using the Serverless Inference API. We also support libraries such as [transformers](https://huggingface.co/models?library=transformers&pipeline_tag=automatic-speech-recognition&sort=downloads), [speechbrain](https://huggingface.co/models?library=speechbrain&pipeline_tag=automatic-speech-recognition&sort=downloads), [NeMo](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&library=nemo&sort=downloads) and [espnet](https://huggingface.co/models?library=espnet&pipeline_tag=automatic-speech-recognition&sort=downloads) via the Serverless Inference API. Here's a simple code snippet to run inference:

```python
import json
Expand All @@ -36,7 +36,19 @@ def query(filename):
data = query("sample1.flac")
```

You can also use libraries such as [transformers](https://huggingface.co/models?library=transformers&pipeline_tag=automatic-speech-recognition&sort=downloads), [speechbrain](https://huggingface.co/models?library=speechbrain&pipeline_tag=automatic-speech-recognition&sort=downloads), [NeMo](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&library=nemo&sort=downloads) and [espnet](https://huggingface.co/models?library=espnet&pipeline_tag=automatic-speech-recognition&sort=downloads) if you want one-click managed Inference without any hassle.
You can also use[huggingface.js](https://github.com/huggingface/huggingface.js), the JavaScript client, to transcribe models with the Inference API.

```javascript
import { HfInference } from "@huggingface/inference";

const inference = new HfInference(HF_TOKEN);
await inference.automaticSpeechRecognition({
data: await (await fetch("sample.flac")).blob(),
model: "openai/whisper-large-v3",
});
```

For transformers compatible models like Whisper, Wav2Vec2, HuBERT, etc. You can also run inference in Python using transformers as follows:

```python
# pip install --upgrade transformers
Expand All @@ -49,18 +61,6 @@ pipe("sample.flac")
# {'text': "GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS"}
```

You can use [huggingface.js](https://github.com/huggingface/huggingface.js) to transcribe text with javascript using models on Hugging Face Hub.

```javascript
import { HfInference } from "@huggingface/inference";

const inference = new HfInference(HF_TOKEN);
await inference.automaticSpeechRecognition({
data: await (await fetch("sample.flac")).blob(),
model: "openai/whisper-large-v3",
});
```

## Solving ASR for your own data

We have some great news! You can fine-tune (transfer learning) a foundational speech model on a specific language without tonnes of data. Pretrained models such as Whisper, Wav2Vec2-MMS and HuBERT exist. [OpenAI's Whisper model](https://huggingface.co/openai/whisper-large-v3) is a large multilingual model trained on 100+ languages and with 4 Million hours of speech.
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