Audio API

TensorX provides OpenAI-compatible audio endpoints for both Text-to-Speech (TTS) and Speech-to-Text (STT) transcription.

Available Models

Model	Type	Description
chatterbox-turbo	TTS	High-quality text-to-speech generation
Systran/faster-whisper-large-v3	STT	Fast, accurate speech transcription

Text-to-Speech (TTS)

Convert text into natural-sounding audio using the chatterbox-turbo model.

Endpoint

POST https://api.tensorx.ai/v1/audio/speech

Basic Usage

curl https://api.tensorx.ai/v1/audio/speech \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "chatterbox-turbo",
    "input": "Hello! Welcome to TensorX.",
    "voice": "Emily.wav"
  }' \
  --output speech.mp3

Parameters

Parameter	Type	Required	Description
model	string	Yes	Model ID: chatterbox-turbo
input	string	Yes	Text to convert to speech. For very long inputs, split into smaller chunks and concatenate the resulting audio.
voice	string	Yes	Voice ID, using the filename of one of the predefined voices below (e.g. Emily.wav). OpenAI-style names like alloy or nova are not supported.
response_format	string	No	Audio format: mp3, wav, opus (default: mp3)
speed	number	No	Playback speed multiplier (default: 1.0). Values around 0.5 to 2.0 give the most natural results.

Available Voices

chatterbox-turbo ships with 28 predefined English voices. Pass the filename (including the .wav suffix) as the voice parameter:

Abigail.wav	Adrian.wav	Alexander.wav	Alice.wav
Austin.wav	Axel.wav	Connor.wav	Cora.wav
Elena.wav	Eli.wav	Emily.wav	Everett.wav
Gabriel.wav	Gianna.wav	Henry.wav	Ian.wav
Jade.wav	Jeremiah.wav	Jordan.wav	Julian.wav
Layla.wav	Leonardo.wav	Michael.wav	Miles.wav
Olivia.wav	Ryan.wav	Taylor.wav	Thomas.wav

The TTS engine is a hosted instance of the open-source Chatterbox-TTS-Server project, which is in turn built on Resemble AI's Chatterbox model. See the upstream documentation for details on the voices, voice characteristics, and how voice cloning works.

Python Example

import requests

response = requests.post(
    "https://api.tensorx.ai/v1/audio/speech",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "chatterbox-turbo",
        "input": "Welcome to TensorX! This is a test of our text-to-speech API.",
        "voice": "Emily.wav"
    }
)

# Save audio file
with open("output.mp3", "wb") as f:
    f.write(response.content)

print(f"Audio saved: {len(response.content)} bytes")

JavaScript/Node.js Example

const fs = require('fs');

async function textToSpeech(text) {
  const response = await fetch('https://api.tensorx.ai/v1/audio/speech', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'chatterbox-turbo',
      input: text,
      voice: 'Emily.wav'
    })
  });

  const buffer = await response.arrayBuffer();
  fs.writeFileSync('output.mp3', Buffer.from(buffer));
  console.log('Audio saved!');
}

textToSpeech('Hello from TensorX!');

Pricing

TTS Cost: $0.000005 per character

Text Length	Cost
100 characters	$0.0005
1,000 characters	$0.005
10,000 characters	$0.05

---

Speech-to-Text (STT)

Transcribe audio files to text using the Systran/faster-whisper-large-v3 model.

Endpoint

POST https://api.tensorx.ai/v1/audio/transcriptions

Basic Usage

curl https://api.tensorx.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F file="@audio.mp3" \
  -F model="Systran/faster-whisper-large-v3"

Parameters

Parameter	Type	Required	Description
file	file	Yes	Audio file to transcribe (mp3, mp4, mpeg, mpga, m4a, wav, webm)
model	string	Yes	Model ID: Systran/faster-whisper-large-v3
language	string	No	Language code (e.g., en, es, fr). Auto-detected if not specified
response_format	string	No	Output format: json, text, srt, vtt, verbose_json (default: json)
timestamp_granularities	array	No	["word"] or ["segment"] for timestamps

Response

{
  "text": "Hello, this is a test of the TensorX Audio Gateway.",
  "language": "en",
  "duration": 5.44,
  "segments": [
    {
      "start": 0.0,
      "end": 2.5,
      "text": "Hello, this is a test"
    },
    {
      "start": 2.5,
      "end": 5.44,
      "text": "of the TensorX Audio Gateway."
    }
  ]
}

Python Example

import requests

# Transcribe audio file
with open("audio.mp3", "rb") as audio_file:
    response = requests.post(
        "https://api.tensorx.ai/v1/audio/transcriptions",
        headers={
            "Authorization": "Bearer YOUR_API_KEY"
        },
        files={
            "file": audio_file
        },
        data={
            "model": "Systran/faster-whisper-large-v3"
        }
    )

result = response.json()
print(f"Transcription: {result['text']}")
print(f"Language: {result.get('language', 'auto')}")
print(f"Duration: {result.get('duration', 'N/A')} seconds")

JavaScript/Node.js Example

const fs = require('fs');
const FormData = require('form-data');

async function transcribeAudio(filePath) {
  const form = new FormData();
  form.append('file', fs.createReadStream(filePath));
  form.append('model', 'Systran/faster-whisper-large-v3');

  const response = await fetch('https://api.tensorx.ai/v1/audio/transcriptions', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: form
  });

  const result = await response.json();
  console.log('Transcription:', result.text);
  return result;
}

transcribeAudio('audio.mp3');

Get Timestamps

Request word or segment-level timestamps:

curl https://api.tensorx.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F file="@audio.mp3" \
  -F model="Systran/faster-whisper-large-v3" \
  -F response_format="verbose_json" \
  -F 'timestamp_granularities[]=segment'

Pricing

STT Cost: $0.0000667 per second of audio

Audio Duration	Cost
1 minute	$0.004
10 minutes	$0.04
1 hour	$0.24

---

Supported Audio Formats

Both TTS and STT support the following audio formats:

Format	Extension	Notes
MP3	.mp3	Most common, good compression
WAV	.wav	Uncompressed, highest quality
M4A	.m4a	Apple audio format
WEBM	.webm	Web-optimized
MPEG	.mpeg, .mpga	Legacy format
MP4	.mp4	Video format (audio extracted)
OGG	.ogg	Open source format

File Size Limits

• Maximum file size: 25 MB
• For larger files, split them into smaller segments

---

Use Cases

Voice Assistants

# Complete voice assistant flow
import requests

def voice_assistant(audio_input_path):
    # 1. Transcribe user's speech
    with open(audio_input_path, "rb") as f:
        transcription = requests.post(
            "https://api.tensorx.ai/v1/audio/transcriptions",
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            files={"file": f},
            data={"model": "Systran/faster-whisper-large-v3"}
        ).json()
    
    user_text = transcription["text"]
    print(f"User said: {user_text}")
    
    # 2. Get AI response (using chat completions)
    # Use any chat model from /v1/models. `openai/gpt-oss-20b`
    # is a fast, low-cost default; swap in another model if you prefer.
    ai_response = requests.post(
        "https://api.tensorx.ai/v1/chat/completions",
        headers={
            "Authorization": "Bearer YOUR_API_KEY",
            "Content-Type": "application/json"
        },
        json={
            "model": "openai/gpt-oss-20b",
            "messages": [{"role": "user", "content": user_text}]
        }
    ).json()
    
    assistant_text = ai_response["choices"][0]["message"]["content"]
    print(f"Assistant: {assistant_text}")
    
    # 3. Convert response to speech
    audio = requests.post(
        "https://api.tensorx.ai/v1/audio/speech",
        headers={
            "Authorization": "Bearer YOUR_API_KEY",
            "Content-Type": "application/json"
        },
        json={
            "model": "chatterbox-turbo",
            "input": assistant_text,
            "voice": "Emily.wav"
        }
    )
    
    with open("response.mp3", "wb") as f:
        f.write(audio.content)
    
    return "response.mp3"

Podcast Transcription

def transcribe_podcast(file_path):
    with open(file_path, "rb") as f:
        response = requests.post(
            "https://api.tensorx.ai/v1/audio/transcriptions",
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            files={"file": f},
            data={
                "model": "Systran/faster-whisper-large-v3",
                "response_format": "srt"  # Get SRT subtitles
            }
        )
    
    # Save as subtitle file
    with open("podcast.srt", "w") as f:
        f.write(response.text)
    
    return "podcast.srt"

Content Narration

def narrate_article(article_text):
    # Split long articles into chunks. There is no hard cap on `input` length,
    # but chunking on sentence boundaries gives more natural pacing and lets
    # you stream playback while later chunks are still being generated.
    max_chars = 4000
    chunks = [article_text[i:i+max_chars] for i in range(0, len(article_text), max_chars)]
    
    audio_parts = []
    for i, chunk in enumerate(chunks):
        response = requests.post(
            "https://api.tensorx.ai/v1/audio/speech",
            headers={
                "Authorization": "Bearer YOUR_API_KEY",
                "Content-Type": "application/json"
            },
            json={
                "model": "chatterbox-turbo",
                "input": chunk,
                "voice": "Emily.wav"
            }
        )
        
        filename = f"part_{i}.mp3"
        with open(filename, "wb") as f:
            f.write(response.content)
        audio_parts.append(filename)
    
    return audio_parts

---

Error Handling

Common Errors

Error Code	Description	Solution
400	Invalid audio format	Use supported format (mp3, wav, etc.)
400	File too large	Split into chunks under 25MB
401	Invalid API key	Check your API key
413	Payload too large	Reduce file size
429	Rate limit exceeded	Reduce request frequency

Python Error Handling

import requests

def safe_transcribe(file_path):
    try:
        with open(file_path, "rb") as f:
            response = requests.post(
                "https://api.tensorx.ai/v1/audio/transcriptions",
                headers={"Authorization": "Bearer YOUR_API_KEY"},
                files={"file": f},
                data={"model": "Systran/faster-whisper-large-v3"}
            )
        
        response.raise_for_status()
        return response.json()
    
    except requests.exceptions.HTTPError as e:
        print(f"HTTP Error: {e.response.status_code}")
        print(f"Details: {e.response.text}")
        return None
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return None

---

Resources

• OpenAI Audio API Reference
• TensorX API Overview
• TensorX Models