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‎docs/api-inference/tasks/audio-classification.md‎

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@@ -40,60 +40,79 @@ Explore all available models and find the one that suits you best [here](https:/
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<inferencesnippet>
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<curl>
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```bash
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curl https://router.huggingface.co/hf-inference/models/speechbrain/google_speech_command_xvector \
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-X POST \
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--data-binary '@sample1.flac' \
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-H 'Authorization: Bearer hf_***'
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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.audio_classification("sample1.flac", model="speechbrain/google_speech_command_xvector")
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```
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</curl>
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<python>
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```py
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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/v1"
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API_URL = "https://router.huggingface.co/hf-inference/models/speechbrain/google_speech_command_xvector"
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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=headers, data=data)
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return response.json()
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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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To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.audio_classification).
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</python>
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</snippet>
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<js>
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<snippet provider="hf-inference" language="js" client="fetch">
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```js
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async function query(filename) {
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const data = fs.readFileSync(filename);
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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/speechbrain/google_speech_command_xvector",
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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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"Content-Type": "audio/flac"
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},
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method: "POST",
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body: data,
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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("sample1.flac").then((response) => {
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console.log(JSON.stringify(response));
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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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To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#audioclassification).
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</js>
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</snippet>
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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/speechbrain/google_speech_command_xvector \
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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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‎docs/api-inference/tasks/automatic-speech-recognition.md‎

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@@ -39,79 +39,100 @@ Explore all available models and find the one that suits you best [here](https:/
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<inferencesnippet>
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<curl>
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```bash
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curl https://router.huggingface.co/hf-inference/models/openai/whisper-large-v3 \
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-X POST \
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--data-binary '@sample1.flac' \
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-H 'Authorization: Bearer hf_***'
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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")
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```
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</curl>
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<python>
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```py
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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/v1"
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API_URL = "https://router.huggingface.co/hf-inference/models/openai/whisper-large-v3"
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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=headers, data=data)
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return response.json()
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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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To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.automatic_speech_recognition).
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</python>
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<js>
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Using `huggingface.js`:
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```js
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import { HfInference } from "@huggingface/inference";
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const client = new HfInference("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: "hf-inference",
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});
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</snippet>
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console.log(output);
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```
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Using `fetch`:
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<snippet provider="hf-inference" language="js" client="fetch">
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```js
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async function query(filename) {
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const data = fs.readFileSync(filename);
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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",
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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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"Content-Type": "audio/flac"
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},
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method: "POST",
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body: data,
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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("sample1.flac").then((response) => {
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console.log(JSON.stringify(response));
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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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To use the JavaScript client, see `huggingface.js`'s [package reference](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#automaticspeechrecognition).
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</js>
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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",
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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 \
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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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