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‎docs/inference-providers/tasks/automatic-speech-recognition.md‎

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@@ -35,208 +35,11 @@ Explore all available models and find the one that suits you best [here](https:/
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### Using the API
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<inferencesnippet>
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<InferenceSnippet
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pipeline=automatic-speech-recognition
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<snippet provider="fal-ai" language="python" client="huggingface_hub">
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient(
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provider="fal-ai",
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api_key="hf_***",
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)
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output = client.automatic_speech_recognition("sample1.flac", model="openai/whisper-large-v3")
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```
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</snippet>
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To use the Python `InferenceClient`, see the [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.).
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<snippet provider="fal-ai" language="python" client="requests">
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```python
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import requests
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API_URL = "https://router.huggingface.co/fal-ai/fal-ai/whisper"
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headers = {"Authorization": "Bearer hf_***"}
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def query(filename):
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with open(filename, "rb") as f:
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data = f.read()
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response = requests.post(API_URL, headers={"Content-Type": "audio/flac", **headers}, data=data)
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return response.json()
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output = query("sample1.flac")
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```
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</snippet>
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<snippet provider="fal-ai" language="js" client="fetch">
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```js
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async function query(data) {
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const response = await fetch(
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"https://router.huggingface.co/fal-ai/fal-ai/whisper",
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{
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headers: {
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Authorization: "Bearer hf_***",
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"Content-Type": "audio/flac"
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},
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method: "POST",
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body: JSON.stringify(data),
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}
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);
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const result = await response.json();
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return result;
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}
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query({ inputs: "sample1.flac" }).then((response) => {
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console.log(JSON.stringify(response));
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});
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```
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</snippet>
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<snippet provider="fal-ai" language="js" client="huggingface.js">
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```js
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import { InferenceClient } from "@huggingface/inference";
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const client = new InferenceClient("hf_***");
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const data = fs.readFileSync("sample1.flac");
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const output = await client.automaticSpeechRecognition({
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data,
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model: "openai/whisper-large-v3",
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provider: "fal-ai",
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});
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console.log(output);
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```
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</snippet>
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To use the JavaScript `InferenceClient`, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/InferenceClient#).
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<snippet provider="fal-ai" language="sh" client="curl">
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```sh
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curl https://router.huggingface.co/fal-ai/fal-ai/whisper \
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-X POST \
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-H 'Authorization: Bearer hf_***' \
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-H 'Content-Type: audio/flac' \
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--data-binary @"sample1.flac"
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```
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</snippet>
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<snippet provider="hf-inference" language="python" client="huggingface_hub">
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient(
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provider="hf-inference",
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api_key="hf_***",
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)
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output = client.automatic_speech_recognition("sample1.flac", model="openai/whisper-large-v3-turbo")
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```
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</snippet>
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To use the Python `InferenceClient`, see the [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.).
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<snippet provider="hf-inference" language="python" client="requests">
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```python
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import requests
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API_URL = "https://router.huggingface.co/hf-inference/models/openai/whisper-large-v3-turbo"
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headers = {"Authorization": "Bearer hf_***"}
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def query(filename):
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with open(filename, "rb") as f:
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data = f.read()
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response = requests.post(API_URL, headers={"Content-Type": "audio/flac", **headers}, data=data)
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return response.json()
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output = query("sample1.flac")
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```
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</snippet>
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<snippet provider="hf-inference" language="js" client="fetch">
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```js
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async function query(data) {
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const response = await fetch(
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"https://router.huggingface.co/hf-inference/models/openai/whisper-large-v3-turbo",
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{
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headers: {
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Authorization: "Bearer hf_***",
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"Content-Type": "audio/flac"
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},
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method: "POST",
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body: JSON.stringify(data),
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}
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);
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const result = await response.json();
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return result;
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}
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query({ inputs: "sample1.flac" }).then((response) => {
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console.log(JSON.stringify(response));
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});
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```
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</snippet>
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<snippet provider="hf-inference" language="js" client="huggingface.js">
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```js
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import { InferenceClient } from "@huggingface/inference";
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const client = new InferenceClient("hf_***");
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const data = fs.readFileSync("sample1.flac");
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const output = await client.automaticSpeechRecognition({
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data,
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model: "openai/whisper-large-v3-turbo",
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provider: "hf-inference",
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});
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console.log(output);
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```
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</snippet>
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To use the JavaScript `InferenceClient`, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/InferenceClient#).
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<snippet provider="hf-inference" language="sh" client="curl">
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```sh
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curl https://router.huggingface.co/hf-inference/models/openai/whisper-large-v3-turbo \
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-X POST \
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-H 'Authorization: Bearer hf_***' \
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-H 'Content-Type: audio/flac' \
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--data-binary @"sample1.flac"
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```
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</snippet>
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</inferencesnippet>
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providersMapping={ {"fal-ai":{"modelId":"openai/whisper-large-v3","providerModelId":"fal-ai/whisper"},"hf-inference":{"modelId":"openai/whisper-large-v3-turbo","providerModelId":"openai/whisper-large-v3-turbo"}} }
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/>
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‎docs/inference-providers/tasks/feature-extraction.md‎

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@@ -35,112 +35,11 @@ Explore all available models and find the one that suits you best [here](https:/
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### Using the API
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<inferencesnippet>
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<InferenceSnippet
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pipeline=feature-extraction
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<snippet provider="hf-inference" language="python" client="huggingface_hub">
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient(
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provider="hf-inference",
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api_key="hf_***",
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)
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result = client.feature_extraction(
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inputs="Today is a sunny day and I will get some ice cream.",
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model="intfloat/multilingual-e5-large-instruct",
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)
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```
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</snippet>
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To use the Python `InferenceClient`, see the [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.).
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<snippet provider="hf-inference" language="python" client="requests">
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```python
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import requests
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API_URL = "https://router.huggingface.co/hf-inference/pipeline/feature-extraction/intfloat/multilingual-e5-large-instruct"
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headers = {"Authorization": "Bearer hf_***"}
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def query(payload):
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response = requests.post(API_URL, headers=headers, json=payload)
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return response.json()
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output = query({
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"inputs": "Today is a sunny day and I will get some ice cream.",
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})
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```
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</snippet>
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<snippet provider="hf-inference" language="js" client="fetch">
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```js
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async function query(data) {
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const response = await fetch(
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"https://router.huggingface.co/hf-inference/pipeline/feature-extraction/intfloat/multilingual-e5-large-instruct",
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{
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headers: {
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Authorization: "Bearer hf_***",
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"Content-Type": "application/json",
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},
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method: "POST",
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body: JSON.stringify(data),
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}
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);
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const result = await response.json();
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return result;
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}
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query({ inputs: "Today is a sunny day and I will get some ice cream." }).then((response) => {
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console.log(JSON.stringify(response));
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});
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```
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</snippet>
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<snippet provider="hf-inference" language="js" client="huggingface.js">
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```js
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import { InferenceClient } from "@huggingface/inference";
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const client = new InferenceClient("hf_***");
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const output = await client.featureExtraction({
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model: "intfloat/multilingual-e5-large-instruct",
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inputs: "Today is a sunny day and I will get some ice cream.",
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provider: "hf-inference",
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});
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console.log(output);
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```
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</snippet>
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To use the JavaScript `InferenceClient`, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/InferenceClient#).
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<snippet provider="hf-inference" language="sh" client="curl">
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```sh
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curl https://router.huggingface.co/hf-inference/pipeline/feature-extraction/intfloat/multilingual-e5-large-instruct \
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-X POST \
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-H 'Authorization: Bearer hf_***' \
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-H 'Content-Type: application/json' \
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-d '{
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"inputs": "\"Today is a sunny day and I will get some ice cream.\""
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}'
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```
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</snippet>
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</inferencesnippet>
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providersMapping={ {"hf-inference":{"modelId":"mixedbread-ai/mxbai-embed-large-v1","providerModelId":"mixedbread-ai/mxbai-embed-large-v1"},"sambanova":{"modelId":"intfloat/e5-mistral-7b-instruct","providerModelId":"E5-Mistral-7B-Instruct"}} }
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/>
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