Noise Reduction

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
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}")

POST /api/v1/denoiser/spectral/denoise
Authorization: Bearer {api_key}
Content-Type: multipart/form-data

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

GET /api/v1/denoiser/jobs/{job_id}
Authorization: Bearer {api_key}

GET /api/v1/denoiser/jobs?limit=50&skip=0
Authorization: Bearer {api_key}

POST /api/v1/denoiser/jobs/{job_id}/retry
Authorization: Bearer {api_key}

POST /api/v1/denoiser/jobs/{job_id}/cancel
Authorization: Bearer {api_key}

DELETE /api/v1/denoiser/jobs/{job_id}
Authorization: Bearer {api_key}

GET /api/v1/denoiser/health
Authorization: Bearer {api_key}