Noise Reduction

Overview

AudioPod AI’s Noise Reduction API uses advanced AI models to remove background noise, enhance speech clarity, and improve overall audio quality. Process recordings to eliminate unwanted sounds while preserving the natural characteristics of speech and music.

Key Features

AI-Powered Denoising: Advanced neural networks for intelligent noise removal
Multiple Quality Modes: Balanced, aggressive, and preserving modes
Spectral Processing: Traditional spectral subtraction for specific noise types
Voice Preservation: Maintain natural speech characteristics during cleanup
Video Support: Process audio from video files while preserving video streams
Batch Processing: Handle multiple files efficiently

Authentication

All endpoints require authentication. Use one of these methods:

API Key (Recommended): X-API-Key: your_api_key header
JWT Token: Authorization: Bearer your_jwt_token (for session-based auth)

AI Noise Reduction

Standard Denoising

Remove background noise using AI-powered denoising models.

Python
Node.js
Raw HTTP
cURL

from audiopod import Client

client = Client()

# Simple noise reduction
denoised = client.denoiser.denoise_audio(
    audio_file="noisy_recording.wav",
    quality_mode="balanced",  # "balanced", "studio"
    wait_for_completion=True
)

print(f"Denoising completed!")
print(f"Input file: {denoised.input_file}")
print(f"Denoised audio URL: {denoised.denoised_audio_url}")
print(f"Noise reduction level: {denoised.noise_reduction_db}dB")

# Download cleaned audio
clean_audio = client.download_file(denoised.denoised_audio_url)
with open("clean_recording.wav", "wb") as f:
    f.write(clean_audio)

# Studio quality denoising
studio_denoised = client.denoiser.denoise_audio(
    audio_file="interview_with_background_noise.wav",
    quality_mode="studio",  # Higher quality processing
    wait_for_completion=True
)

print(f"Studio denoising completed!")
print(f"Noise reduction level: {studio_denoised.noise_reduction_db}dB")

# Batch processing multiple files
noisy_files = [
    "meeting_room_recording.wav",
    "interview_outdoors.mp3",
    "podcast_home_setup.wav",
    "lecture_hall_audio.m4a"
]

batch_results = client.denoiser.denoise_batch(
    audio_files=noisy_files,
    quality_mode="balanced",
    wait_for_completion=True
)

for i, result in enumerate(batch_results):
    print(f"\nFile {i+1}: {noisy_files[i]}")
    print(f"Status: {result.status}")
    if result.status == "completed":
        print(f"Noise reduced: {result.noise_reduction_db}dB")
        print(f"Clean audio: {result.denoised_audio_url}")
        
        # Download and save
        clean_audio = client.download_file(result.denoised_audio_url)
        output_name = f"clean_{i+1}_{noisy_files[i].split('.')[0]}.wav"
        with open(output_name, "wb") as f:
            f.write(clean_audio)
        print(f"Saved: {output_name}")

# Process video files (audio cleaning)
video_denoised = client.denoiser.denoise_video(
    video_file="presentation_with_noise.mp4",
    quality_mode="studio",  # Higher quality processing
    preserve_video=True,        # Keep original video stream
    wait_for_completion=True
)

print(f"Video denoising completed!")
print(f"Clean video URL: {video_denoised.video_output_url}")
print(f"Audio-only URL: {video_denoised.denoised_audio_url}")

// Note: SDK examples shown are conceptual and may not reflect the current SDK implementation
// For production use, refer to the Raw HTTP examples or check the latest SDK documentation
const { AudioPodClient } = require('audiopod-js');
const fs = require('fs');

const client = new AudioPodClient();

async function denoiseAudio() {
  try {
    // Simple noise reduction
    const denoised = await client.denoiser.denoiseAudio({
      audioFile: fs.createReadStream('noisy_recording.wav'),
      qualityMode: 'balanced',  // "balanced", "studio"
      waitForCompletion: true
    });

    console.log('Denoising completed!');
    console.log(`Input file: ${denoised.inputFile}`);
    console.log(`Denoised audio URL: ${denoised.denoisedAudioUrl}`);
    console.log(`Noise reduction level: ${denoised.noiseReductionDb}dB`);

    // Download cleaned audio
    const cleanAudio = await client.downloadFile(denoised.denoisedAudioUrl);
    fs.writeFileSync('clean_recording.wav', cleanAudio);

    // Studio quality denoising
    const studioDenoised = await client.denoiser.denoiseAudio({
      audioFile: fs.createReadStream('interview_with_background_noise.wav'),
      qualityMode: 'studio',  // Higher quality processing
      waitForCompletion: true
    });

    console.log('Studio denoising completed!');
    console.log(`Noise reduction level: ${studioDenoised.noiseReductionDb}dB`);

    // Batch processing multiple files
    const noisyFiles = [
      'meeting_room_recording.wav',
      'interview_outdoors.mp3',
      'podcast_home_setup.wav',
      'lecture_hall_audio.m4a'
    ];

    const fileStreams = noisyFiles.map(file => ({
      file: fs.createReadStream(file),
      name: file
    }));

    const batchResults = await client.denoiser.denoiseBatch({
      audioFiles: fileStreams,
      qualityMode: 'balanced',
      waitForCompletion: true
    });

    batchResults.forEach((result, index) => {
      console.log(`\nFile ${index + 1}: ${noisyFiles[index]}`);
      console.log(`Status: ${result.status}`);
      if (result.status === 'completed') {
        console.log(`Noise reduced: ${result.noiseReductionDb}dB`);
        console.log(`Clean audio: ${result.denoisedAudioUrl}`);
        
        // Download and save
        client.downloadFile(result.denoisedAudioUrl).then(cleanAudio => {
          const outputName = `clean_${index + 1}_${noisyFiles[index].split('.')[0]}.wav`;
          fs.writeFileSync(outputName, cleanAudio);
          console.log(`Saved: ${outputName}`);
        });
      }
    });

    // Process video files (audio cleaning)
    const videoDenoised = await client.denoiser.denoiseVideo({
      videoFile: fs.createReadStream('presentation_with_noise.mp4'),
      qualityMode: 'studio',  // Higher quality processing
      preserveVideo: true,        // Keep original video stream
      waitForCompletion: true
    });

    console.log('Video denoising completed!');
    console.log(`Clean video URL: ${videoDenoised.videoOutputUrl}`);
    console.log(`Audio-only URL: ${videoDenoised.denoisedAudioUrl}`);

  } catch (error) {
    console.error('Denoising error:', error.message);
  }
}

denoiseAudio();

import requests
import time

# Standard denoising
with open("noisy_recording.wav", "rb") as audio_file:
    response = requests.post(
        "https://api.audiopod.ai/api/v1/denoiser/denoise",
        headers={"X-API-Key": api_key},
        data={
            "quality_mode": "balanced"  # "balanced", "studio"
        },
        files={"file": audio_file}
    )

if response.status_code == 200:
    job_data = response.json()
    job_id = job_data["id"]
    print(f"Denoising job created: {job_id}")
    
    # Poll for completion
    while True:
        status_response = requests.get(
            f"https://api.audiopod.ai/api/v1/denoiser/jobs/{job_id}",
            headers={"X-API-Key": api_key}
        )
        
        if status_response.status_code == 200:
            job_status = status_response.json()
            print(f"Status: {job_status['status']}")
            
            if job_status["status"] == "COMPLETED":
                print(f"Denoising completed!")
                print(f"Clean audio URL: {job_status.get('denoised_audio_url')}")
                print(f"Noise reduction: {job_status.get('noise_reduction_db', 0)}dB")
                
                # Download the cleaned audio
                if job_status.get('denoised_audio_url'):
                    audio_response = requests.get(job_status['denoised_audio_url'])
                    with open("clean_recording.wav", "wb") as f:
                        f.write(audio_response.content)
                    print("Downloaded clean audio: clean_recording.wav")
                break
            elif job_status["status"] == "FAILED":
                print(f"Denoising failed: {job_status.get('error', 'Unknown error')}")
                break
                
        time.sleep(5)  # Check every 5 seconds
else:
    print(f"Error: {response.status_code} - {response.text}")

# Upload file for denoising
curl -X POST "https://api.audiopod.ai/api/v1/denoiser/denoise" \
  -H "X-API-Key: $AUDIOPOD_API_KEY" \
  -F "file=@noisy_recording.wav" \
  -F "quality_mode=balanced"

# Check job status (replace JOB_ID with actual job ID)
curl -X GET "https://api.audiopod.ai/api/v1/denoiser/jobs/JOB_ID" \
  -H "X-API-Key: $AUDIOPOD_API_KEY"

# Studio quality denoising
curl -X POST "https://api.audiopod.ai/api/v1/denoiser/denoise" \
  -H "X-API-Key: $AUDIOPOD_API_KEY" \
  -F "file=@very_noisy_audio.wav" \
  -F "quality_mode=studio"

Denoise from URL

Process audio/video from URLs (YouTube, etc.) to remove noise.

Python
Node.js
Raw HTTP
cURL

# Note: SDK examples shown are conceptual and may not reflect the current SDK implementation
# For production use, refer to the Raw HTTP examples or check the latest SDK documentation
from audiopod import Client

client = Client()

# Denoise from YouTube URL
url_denoised = client.denoiser.denoise_from_url(
    url="https://youtube.com/watch?v=example123",
    quality_mode="studio",  # Higher quality processing for online content
    wait_for_completion=True
)

print(f"URL denoising completed!")
print(f"Source: {url_denoised.source_url}")
print(f"Clean audio URL: {url_denoised.denoised_audio_url}")
print(f"Noise reduction: {url_denoised.noise_reduction_db}dB")

# Batch URL processing
urls = [
    "https://youtube.com/watch?v=lecture1",
    "https://vimeo.com/meeting_recording",
    "https://example.com/noisy_podcast.mp3"
]

batch_url_results = client.denoiser.denoise_batch_from_urls(
    urls=urls,
    quality_mode="balanced",
    wait_for_completion=True
)

for i, result in enumerate(batch_url_results):
    print(f"\nURL {i+1}: {urls[i]}")
    print(f"Status: {result.status}")
    if result.status == "completed":
        print(f"Clean audio: {result.denoised_audio_url}")

// Note: SDK examples shown are conceptual and may not reflect the current SDK implementation
// For production use, refer to the Raw HTTP examples or check the latest SDK documentation
const { AudioPodClient } = require('audiopod-js');

const client = new AudioPodClient();

async function denoiseFromUrl() {
  try {
    // Denoise from YouTube URL
    const urlDenoised = await client.denoiser.denoiseFromUrl({
      url: "https://youtube.com/watch?v=example123",
      qualityMode: "studio",  // Higher quality processing for online content
      waitForCompletion: true
    });

    console.log('URL denoising completed!');
    console.log(`Source: ${urlDenoised.sourceUrl}`);
    console.log(`Clean audio URL: ${urlDenoised.denoisedAudioUrl}`);
    console.log(`Noise reduction: ${urlDenoised.noiseReductionDb}dB`);

    // Batch URL processing
    const urls = [
      "https://youtube.com/watch?v=lecture1",
      "https://vimeo.com/meeting_recording",
      "https://example.com/noisy_podcast.mp3"
    ];

    const batchUrlResults = await client.denoiser.denoiseBatchFromUrls({
      urls: urls,
      qualityMode: "balanced",
      waitForCompletion: true
    });

    batchUrlResults.forEach((result, index) => {
      console.log(`\nURL ${index + 1}: ${urls[index]}`);
      console.log(`Status: ${result.status}`);
      if (result.status === "completed") {
        console.log(`Clean audio: ${result.denoisedAudioUrl}`);
      }
    });

  } catch (error) {
    console.error('URL denoising error:', error.message);
  }
}

denoiseFromUrl();

import requests
import time

response = requests.post(
    "https://api.audiopod.ai/api/v1/denoiser/denoise",
    headers={"X-API-Key": api_key},
    data={
        "url": "https://youtube.com/watch?v=example123",
        "quality_mode": "studio"  # Higher quality processing
    }
)

if response.status_code == 200:
    job_data = response.json()
    job_id = job_data["id"]
    print(f"URL denoising started: {job_id}")
    
    # Poll for completion
    while True:
        status_response = requests.get(
            f"https://api.audiopod.ai/api/v1/denoiser/jobs/{job_id}",
            headers={"X-API-Key": api_key}
        )
        
        if status_response.status_code == 200:
            job_status = status_response.json()
            print(f"Status: {job_status['status']}")
            
            if job_status["status"] == "COMPLETED":
                print(f"URL denoising completed!")
                print(f"Source: {job_status.get('source_url')}")
                print(f"Clean audio: {job_status.get('denoised_audio_url')}")
                break
            elif job_status["status"] == "FAILED":
                print(f"Denoising failed: {job_status.get('error')}")
                break
                
        time.sleep(10)  # URL processing may take longer

# Denoise from URL
curl -X POST "https://api.audiopod.ai/api/v1/denoiser/denoise" \
  -H "X-API-Key: $AUDIOPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://youtube.com/watch?v=example123",
    "quality_mode": "aggressive"
  }'

# Check job status
curl -X GET "https://api.audiopod.ai/api/v1/denoiser/jobs/JOB_ID" \
  -H "X-API-Key: $AUDIOPOD_API_KEY"

Quality Modes:

balanced: Good balance between noise removal and audio preservation
studio: Higher quality processing with enhanced algorithms, best for professional use

Response:

{
  "id": 123,
  "status": "PROCESSING",
  "progress": 0,
  "input_path": "/uploads/noisy_recording.wav",
  "is_video": false,
  "task_id": "celery_task_uuid_here",
  "created_at": "2024-01-15T10:30:00Z",
  "original_filename": "noisy_recording.wav",
  "display_name": "noisy_recording.wav"
}

Spectral Noise Reduction

Advanced Spectral Processing

Use traditional spectral subtraction for specific noise types and fine control.

POST
Python

POST /api/v1/denoiser/spectral/denoise
X-API-Key: {api_key}
Content-Type: multipart/form-data

file: (audio file)
noise_type: general
prop_decrease: 0.8
stationary: true

with open("recording_with_hum.wav", "rb") as audio_file:
response = requests.post(
        "https://api.audiopod.ai/api/v1/denoiser/spectral/denoise",
        headers={"X-API-Key": api_key},
        data={
            "noise_type": "electrical_hum",  # Specific noise type
            "prop_decrease": 0.8,            # 80% noise reduction
            "stationary": True,              # Constant noise
            "time_constant_s": 2.0,          # Adaptation time
            "freq_mask_smooth_hz": 500,      # Frequency smoothing
            "time_mask_smooth_ms": 50        # Time smoothing
        },
        files={"file": audio_file}
    )

if response.status_code == 200:
    job_data = response.json()
    print(f"Spectral denoising job: {job_data['id']}")

Noise Types:

general: General background noise
electrical_hum: 50/60Hz electrical interference
air_conditioning: HVAC and fan noise
traffic: Vehicle and road noise
crowd: Background conversations and crowd noise

Job Management

Get Job Status

Monitor the progress of noise reduction jobs.

GET
Python

GET /api/v1/denoiser/jobs/{job_id}
X-API-Key: {api_key}

response = requests.get(
    f"https://api.audiopod.ai/api/v1/denoiser/jobs/{job_id}",
    headers={"X-API-Key": api_key}
)

if response.status_code == 200:
    job_status = response.json()
    print(f"Status: {job_status['status']}")
    print(f"Progress: {job_status['progress']}%")
    
    if job_status["status"] == "COMPLETED":
        print("Denoising complete!")
        print(f"Output URL: {job_status['output_url']}")

Response (Completed):

{
  "id": 123,
  "status": "COMPLETED",
  "progress": 100,
  "input_path": "/uploads/noisy_recording.wav",
  "output_path": "/processed/clean_recording_123.wav",
  "video_output_path": null,
  "is_video": false,
  "task_id": "celery_task_uuid_here",
  "created_at": "2024-01-15T10:30:00Z",
  "updated_at": "2024-01-15T10:32:15Z",
  "completed_at": "2024-01-15T10:32:15Z",
  "output_url": "https://api.audiopod.ai/download/clean_recording_123.wav",
  "video_output_url": null,
  "original_filename": "noisy_recording.wav",
  "display_name": "Clean Recording",
  "stats": {
    "noise_reduction_db": 15.2,
    "processing_time": 135.4,
    "audio_duration": 180.0
  }
}

List Denoising Jobs

Get all noise reduction jobs for the authenticated user.

GET
Python

GET /api/v1/denoiser/jobs?limit=50&skip=0
X-API-Key: {api_key}

response = requests.get(
    "https://api.audiopod.ai/api/v1/denoiser/jobs",
    headers={"X-API-Key": api_key},
    params={
        "limit": 50,
        "skip": 0
    }
)

if response.status_code == 200:
    jobs = response.json()
for job in jobs:
        print(f"Job {job['id']}: {job['status']} - {job['original_filename']}")

Retry Failed Job

Retry a failed noise reduction job.

POST

POST /api/v1/denoiser/jobs/{job_id}/retry
X-API-Key: {api_key}

Cancel Job

Cancel a pending denoising job.

POST
Python

POST /api/v1/denoiser/jobs/{job_id}/cancel
X-API-Key: {api_key}

response = requests.post(
    f"https://api.audiopod.ai/api/v1/denoiser/jobs/{job_id}/cancel",
    headers={"X-API-Key": api_key}
)

if response.status_code == 200:
    job_status = response.json()
    print(f"Job {job_id} cancelled: {job_status['status']}")

Delete Job

Remove a completed job and its results.

DELETE
Python

DELETE /api/v1/denoiser/jobs/{job_id}
X-API-Key: {api_key}

response = requests.delete(
    f"https://api.audiopod.ai/api/v1/denoiser/jobs/{job_id}",
    headers={"X-API-Key": api_key}
)

if response.status_code == 204:
    print(f"Job {job_id} and associated files deleted successfully")

Health Check

Check the denoising service availability and status.

GET
Python

GET /api/v1/denoiser/health
X-API-Key: {api_key}

response = requests.get(
    "https://api.audiopod.ai/api/v1/denoiser/health",
    headers={"X-API-Key": api_key}
)

if response.status_code == 200:
    health_status = response.json()
    print(f"Service status: {health_status['status']}")
    print(f"Device: {health_status['device']}")
    print(f"Model: {health_status['model']}")

Health Response:

{
  "status": "available",
  "device": "cuda:0",
  "model": "dns64",
  "cuda_available": true,
  "cuda_device_count": 1,
  "cuda_device_name": "NVIDIA GeForce RTX 4090"
}

Error Handling

400 Bad Request - Invalid Audio

Causes: - Audio file corrupted or invalid format - File contains no audio content - Unsupported codec Solutions: - Use supported formats (WAV, MP3, M4A, MP4) - Verify file integrity - Check audio content exists

422 Processing Error - Denoising Failed

Causes: - Audio quality too poor for processing - Extreme noise levels - Very short audio duration Solutions: - Try different quality mode - Use spectral processing for specific noise types - Ensure minimum 5 seconds duration

402 Payment Required - Insufficient Credits

Causes: - Not enough credits for processing duration Solutions: - Purchase additional credits - Check credit requirements before processing

Pricing

Noise reduction is billed from your account credit balance (subscription plan). The API-wallet (USD) path is not enabled for denoising — calls must authenticate via JWT/session, not via API key.

Service	Quality mode	Cost
AI Denoising	`balanced` (default)	660 credits/minute
AI Denoising	`studio`	1,320 credits/minute
AI Denoising	`ultra`	2,640 credits/minute
Spectral Processing	—	660 credits/minute

Cost Examples

Duration	Method	Credits
10 minutes	AI Denoising (balanced)	6,600
10 minutes	AI Denoising (studio)	13,200
10 minutes	AI Denoising (ultra)	26,400
30 minutes	Spectral	19,800
1 hour	AI Denoising (balanced)	39,600

Overview

Key Features

Authentication

AI Noise Reduction

Standard Denoising

Denoise from URL

Spectral Noise Reduction

Advanced Spectral Processing

Job Management

Get Job Status

List Denoising Jobs

Retry Failed Job

Cancel Job

Delete Job

Health Check

Error Handling

Pricing

Cost Examples

Next Steps

Voice Cloning

Speech-to-Text

​Overview

​Key Features

​Authentication

​AI Noise Reduction

​Standard Denoising

​Denoise from URL

​Spectral Noise Reduction

​Advanced Spectral Processing

​Job Management

​Get Job Status

​List Denoising Jobs

​Retry Failed Job

​Cancel Job

​Delete Job

​Health Check

​Error Handling

​Pricing

​Cost Examples

​Next Steps

Voice Cloning

Speech-to-Text

Overview

Key Features

Authentication

AI Noise Reduction

Standard Denoising

Denoise from URL

Spectral Noise Reduction

Advanced Spectral Processing

Job Management

Get Job Status

List Denoising Jobs

Retry Failed Job

Cancel Job

Delete Job

Health Check

Error Handling

Pricing

Cost Examples

Next Steps