docs: fix ambiguity in asr tasks page. (#724)

Co-authored-by: Omar Sanseviero <[email protected]>

Author: Vaibhavs10

packages/tasks/src/tasks/automatic-speech-recognition/about.md

@@ -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
@@ -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
@@ -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.