音频（实验性质）#

学习如何使用 Xinference 将音频转换为文本或将文本转换为音频。

介绍#

Audio API提供了三种与音频交互的方法：

转录终端将音频转录为输入语言。
翻译端点将音频转换为英文。
转录终端将音频转录为输入语言。

API 端点	OpenAI 兼容端点
Transcription API	/v1/audio/transcriptions
Translation API	/v1/audio/translations
Speech API	/v1/audio/speech

支持的模型列表#

在Xinference中，以下模型支持音频API：

whisper-tiny
whisper-tiny.en
whisper-base
whisper-base.en
whisper-medium
whisper-medium.en
whisper-large-v3
Belle-distilwhisper-large-v2-zh
Belle-whisper-large-v2-zh
Belle-whisper-large-v3-zh
ChatTTS
CosyVoice

快速入门#

转录#

Transcription API 模仿了 OpenAI 的 create transcriptions API。你可以通过 cURL、OpenAI Client 或者 Xinference 的 Python 客户端来尝试 Transcription API：

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/audio/transcriptions' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "file": "<audio bytes>",
  }'

import openai

client = openai.Client(
    api_key="cannot be empty",
    base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
with open("speech.mp3", "rb") as audio_file:
    client.audio.transcriptions.create(
        model=<MODEL_UID>,
        file=audio_file,
    )

from xinference.client import Client

client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")

model = client.get_model("<MODEL_UID>")
with open("speech.mp3", "rb") as audio_file:
    model.transcriptions(audio=audio_file.read())

{
  "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
}

翻译#

Translation API 模仿了 OpenAI 的 create translations API。你可以通过 cURL、OpenAI Client 或 Xinference 的 Python 客户端来尝试使用 Translation API：

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/audio/translations' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "file": "<audio bytes>",
  }'

import openai

client = openai.Client(
    api_key="cannot be empty",
    base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
with open("speech.mp3", "rb") as audio_file:
    client.audio.translations.create(
        model=<MODEL_UID>,
        file=audio_file,
    )

from xinference.client import Client

client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")

model = client.get_model("<MODEL_UID>")
with open("speech.mp3", "rb") as audio_file:
    model.translations(audio=audio_file.read())

{
  "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}

语音#

Transcription API 模仿了 OpenAI 的 create speech API。你可以通过 cURL、OpenAI Client 或者 Xinference 的 Python 客户端来尝试 Speech API：

Speech API use non-stream by default as

The stream output of ChatTTS is not as good as the non-stream output, please refer to: 2noise/ChatTTS#564
The stream requires ffmpeg<7: https://pytorch.org/audio/stable/installation.html#optional-dependencies

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/audio/speech' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "text": "<The text to generate audio for>",
    "voice": "echo",
    "stream": True,
  }'

import openai

client = openai.Client(
    api_key="cannot be empty",
    base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
client.audio.speech.create(
    model=<MODEL_UID>,
    input=<The text to generate audio for>,
    voice="echo",
)

from xinference.client import Client

client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")

model = client.get_model("<MODEL_UID>")
model.speech(
    input=<The text to generate audio for>,
    voice="echo",
    stream: True,
)

The output will be an audio binary.