Prediction

Model

nateraw/audio-super-resolution:9c3d3e39

qqynzilbxm2t5h6eaywx2p7g54

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

Created

about 2 years ago

Webhook

–

Input

input_file: https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav
ddim_steps: 50
guidance_scale: 3.5
seed: 42

{
  "ddim_steps": 50,
  "guidance_scale": 3.5,
  "input_file": "https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav",
  "seed": 42
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_CDQ**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run nateraw/audio-super-resolution using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nateraw/audio-super-resolution:9c3d3e39fb0cb6aea677264881d8073f835336137b39fdea4e94093319379535",
  {
    input: {
      ddim_steps: 50,
      guidance_scale: 3.5,
      input_file: "https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav",
      seed: 42
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_CDQ**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run nateraw/audio-super-resolution using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nateraw/audio-super-resolution:9c3d3e39fb0cb6aea677264881d8073f835336137b39fdea4e94093319379535",
    input={
        "ddim_steps": 50,
        "guidance_scale": 3.5,
        "input_file": "https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav",
        "seed": 42
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_CDQ**********************************

This is your API token. Keep it to yourself.

Run nateraw/audio-super-resolution using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "nateraw/audio-super-resolution:9c3d3e39fb0cb6aea677264881d8073f835336137b39fdea4e94093319379535",
    "input": {
      "ddim_steps": 50,
      "guidance_scale": 3.5,
      "input_file": "https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav",
      "seed": 42
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "qqynzilbxm2t5h6eaywx2p7g54",
  "model": "nateraw/audio-super-resolution",
  "version": "9c3d3e39fb0cb6aea677264881d8073f835336137b39fdea4e94093319379535",
  "input": {
    "ddim_steps": 50,
    "guidance_scale": 3.5,
    "input_file": "https://replicate.delivery/pbxt/JYv70XQsiZBbSmknfMhGoEb4QYbuyJ9hJkfgjyzCvh4TzPmT/music.wav",
    "seed": 42
  },
  "logs": "\u001b[93m Warning: audio is longer than 10.24 seconds, may degrade the model performance. It's recommand to truncate your audio to 5.12 seconds before input to AudioSR to get the best performance.\u001b[00m\n/src/audiosr/utils.py:109: FutureWarning: Pass sr=48000, n_fft=2048, n_mels=256, fmin=20, fmax=24000 as keyword args. From version 0.10 passing these as positional arguments will result in an error\nmel = librosa_mel_fn(sampling_rate, filter_length, n_mel, mel_fmin, mel_fmax)\nRunning DDIM Sampling with 50 timesteps\nDDIM Sampler:   0%|          | 0/50 [00:00<?, ?it/s]\nDDIM Sampler:   2%|▏         | 1/50 [00:06<05:20,  6.54s/it]\nDDIM Sampler:   4%|▍         | 2/50 [00:06<02:14,  2.80s/it]\nDDIM Sampler:   6%|▌         | 3/50 [00:06<01:15,  1.60s/it]\nDDIM Sampler:   8%|▊         | 4/50 [00:07<00:47,  1.04s/it]\nDDIM Sampler:  10%|█         | 5/50 [00:07<00:32,  1.37it/s]\nDDIM Sampler:  12%|█▏        | 6/50 [00:07<00:23,  1.84it/s]\nDDIM Sampler:  14%|█▍        | 7/50 [00:07<00:18,  2.36it/s]\nDDIM Sampler:  16%|█▌        | 8/50 [00:07<00:14,  2.89it/s]\nDDIM Sampler:  18%|█▊        | 9/50 [00:07<00:12,  3.40it/s]\nDDIM Sampler:  20%|██        | 10/50 [00:08<00:10,  3.86it/s]\nDDIM Sampler:  22%|██▏       | 11/50 [00:08<00:09,  4.26it/s]\nDDIM Sampler:  24%|██▍       | 12/50 [00:08<00:08,  4.59it/s]\nDDIM Sampler:  26%|██▌       | 13/50 [00:08<00:07,  4.85it/s]\nDDIM Sampler:  28%|██▊       | 14/50 [00:08<00:07,  5.04it/s]\nDDIM Sampler:  30%|███       | 15/50 [00:09<00:06,  5.19it/s]\nDDIM Sampler:  32%|███▏      | 16/50 [00:09<00:06,  5.29it/s]\nDDIM Sampler:  34%|███▍      | 17/50 [00:09<00:06,  5.37it/s]\nDDIM Sampler:  36%|███▌      | 18/50 [00:09<00:05,  5.42it/s]\nDDIM Sampler:  38%|███▊      | 19/50 [00:09<00:05,  5.46it/s]\nDDIM Sampler:  40%|████      | 20/50 [00:09<00:05,  5.49it/s]\nDDIM Sampler:  42%|████▏     | 21/50 [00:10<00:05,  5.51it/s]\nDDIM Sampler:  44%|████▍     | 22/50 [00:10<00:05,  5.53it/s]\nDDIM Sampler:  46%|████▌     | 23/50 [00:10<00:04,  5.54it/s]\nDDIM Sampler:  48%|████▊     | 24/50 [00:10<00:04,  5.55it/s]\nDDIM Sampler:  50%|█████     | 25/50 [00:10<00:04,  5.55it/s]\nDDIM Sampler:  52%|█████▏    | 26/50 [00:11<00:04,  5.56it/s]\nDDIM Sampler:  54%|█████▍    | 27/50 [00:11<00:04,  5.56it/s]\nDDIM Sampler:  56%|█████▌    | 28/50 [00:11<00:03,  5.56it/s]\nDDIM Sampler:  58%|█████▊    | 29/50 [00:11<00:03,  5.56it/s]\nDDIM Sampler:  60%|██████    | 30/50 [00:11<00:03,  5.56it/s]\nDDIM Sampler:  62%|██████▏   | 31/50 [00:11<00:03,  5.56it/s]\nDDIM Sampler:  64%|██████▍   | 32/50 [00:12<00:03,  5.48it/s]\nDDIM Sampler:  66%|██████▌   | 33/50 [00:12<00:03,  5.50it/s]\nDDIM Sampler:  68%|██████▊   | 34/50 [00:12<00:02,  5.44it/s]\nDDIM Sampler:  70%|███████   | 35/50 [00:12<00:02,  5.23it/s]\nDDIM Sampler:  72%|███████▏  | 36/50 [00:12<00:02,  5.08it/s]\nDDIM Sampler:  74%|███████▍  | 37/50 [00:13<00:02,  4.99it/s]\nDDIM Sampler:  76%|███████▌  | 38/50 [00:13<00:02,  4.85it/s]\nDDIM Sampler:  78%|███████▊  | 39/50 [00:13<00:02,  4.74it/s]\nDDIM Sampler:  80%|████████  | 40/50 [00:13<00:02,  4.71it/s]\nDDIM Sampler:  82%|████████▏ | 41/50 [00:13<00:01,  4.69it/s]\nDDIM Sampler:  84%|████████▍ | 42/50 [00:14<00:01,  4.68it/s]\nDDIM Sampler:  86%|████████▌ | 43/50 [00:14<00:01,  4.68it/s]\nDDIM Sampler:  88%|████████▊ | 44/50 [00:14<00:01,  4.81it/s]\nDDIM Sampler:  90%|█████████ | 45/50 [00:14<00:00,  5.01it/s]\nDDIM Sampler:  92%|█████████▏| 46/50 [00:14<00:00,  5.16it/s]\nDDIM Sampler:  94%|█████████▍| 47/50 [00:15<00:00,  5.28it/s]\nDDIM Sampler:  96%|█████████▌| 48/50 [00:15<00:00,  5.36it/s]\nDDIM Sampler:  98%|█████████▊| 49/50 [00:15<00:00,  5.42it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:15<00:00,  5.46it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:15<00:00,  3.19it/s]",
  "output": "https://pbxt.replicate.delivery/73xEQwFed8UXSCQpBJuLYgg8ym0vjp2nSidaqWKwcxhUvHzIA/out.wav",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-09-20T19:17:04.700648Z",
  "started_at": "2023-09-20T19:18:33.274001Z",
  "completed_at": "2023-09-20T19:19:06.183902Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/qqynzilbxm2t5h6eaywx2p7g54/cancel",
    "get": "https://api.replicate.com/v1/predictions/qqynzilbxm2t5h6eaywx2p7g54"
  },
  "metrics": {
    "predict_time": 32.909901,
    "total_time": 121.483254
  }
}

Generated in

32.9 seconds

Tweak it Report

[93m Warning: audio is longer than 10.24 seconds, may degrade the model performance. It's recommand to truncate your audio to 5.12 seconds before input to AudioSR to get the best performance.[00m /src/audiosr/utils.py:109: FutureWarning: Pass sr=48000, n_fft=2048, n_mels=256, fmin=20, fmax=24000 as keyword args. From version 0.10 passing these as positional arguments will result in an error mel = librosa_mel_fn(sampling_rate, filter_length, n_mel, mel_fmin, mel_fmax) Running DDIM Sampling with 50 timesteps DDIM Sampler: 0%| | 0/50 [00:00<?, ?it/s] DDIM Sampler: 2%|▏ | 1/50 [00:06<05:20, 6.54s/it] DDIM Sampler: 4%|▍ | 2/50 [00:06<02:14, 2.80s/it] DDIM Sampler: 6%|▌ | 3/50 [00:06<01:15, 1.60s/it] DDIM Sampler: 8%|▊ | 4/50 [00:07<00:47, 1.04s/it] DDIM Sampler: 10%|█ | 5/50 [00:07<00:32, 1.37it/s] DDIM Sampler: 12%|█▏ | 6/50 [00:07<00:23, 1.84it/s] DDIM Sampler: 14%|█▍ | 7/50 [00:07<00:18, 2.36it/s] DDIM Sampler: 16%|█▌ | 8/50 [00:07<00:14, 2.89it/s] DDIM Sampler: 18%|█▊ | 9/50 [00:07<00:12, 3.40it/s] DDIM Sampler: 20%|██ | 10/50 [00:08<00:10, 3.86it/s] DDIM Sampler: 22%|██▏ | 11/50 [00:08<00:09, 4.26it/s] DDIM Sampler: 24%|██▍ | 12/50 [00:08<00:08, 4.59it/s] DDIM Sampler: 26%|██▌ | 13/50 [00:08<00:07, 4.85it/s] DDIM Sampler: 28%|██▊ | 14/50 [00:08<00:07, 5.04it/s] DDIM Sampler: 30%|███ | 15/50 [00:09<00:06, 5.19it/s] DDIM Sampler: 32%|███▏ | 16/50 [00:09<00:06, 5.29it/s] DDIM Sampler: 34%|███▍ | 17/50 [00:09<00:06, 5.37it/s] DDIM Sampler: 36%|███▌ | 18/50 [00:09<00:05, 5.42it/s] DDIM Sampler: 38%|███▊ | 19/50 [00:09<00:05, 5.46it/s] DDIM Sampler: 40%|████ | 20/50 [00:09<00:05, 5.49it/s] DDIM Sampler: 42%|████▏ | 21/50 [00:10<00:05, 5.51it/s] DDIM Sampler: 44%|████▍ | 22/50 [00:10<00:05, 5.53it/s] DDIM Sampler: 46%|████▌ | 23/50 [00:10<00:04, 5.54it/s] DDIM Sampler: 48%|████▊ | 24/50 [00:10<00:04, 5.55it/s] DDIM Sampler: 50%|█████ | 25/50 [00:10<00:04, 5.55it/s] DDIM Sampler: 52%|█████▏ | 26/50 [00:11<00:04, 5.56it/s] DDIM Sampler: 54%|█████▍ | 27/50 [00:11<00:04, 5.56it/s] DDIM Sampler: 56%|█████▌ | 28/50 [00:11<00:03, 5.56it/s] DDIM Sampler: 58%|█████▊ | 29/50 [00:11<00:03, 5.56it/s] DDIM Sampler: 60%|██████ | 30/50 [00:11<00:03, 5.56it/s] DDIM Sampler: 62%|██████▏ | 31/50 [00:11<00:03, 5.56it/s] DDIM Sampler: 64%|██████▍ | 32/50 [00:12<00:03, 5.48it/s] DDIM Sampler: 66%|██████▌ | 33/50 [00:12<00:03, 5.50it/s] DDIM Sampler: 68%|██████▊ | 34/50 [00:12<00:02, 5.44it/s] DDIM Sampler: 70%|███████ | 35/50 [00:12<00:02, 5.23it/s] DDIM Sampler: 72%|███████▏ | 36/50 [00:12<00:02, 5.08it/s] DDIM Sampler: 74%|███████▍ | 37/50 [00:13<00:02, 4.99it/s] DDIM Sampler: 76%|███████▌ | 38/50 [00:13<00:02, 4.85it/s] DDIM Sampler: 78%|███████▊ | 39/50 [00:13<00:02, 4.74it/s] DDIM Sampler: 80%|████████ | 40/50 [00:13<00:02, 4.71it/s] DDIM Sampler: 82%|████████▏ | 41/50 [00:13<00:01, 4.69it/s] DDIM Sampler: 84%|████████▍ | 42/50 [00:14<00:01, 4.68it/s] DDIM Sampler: 86%|████████▌ | 43/50 [00:14<00:01, 4.68it/s] DDIM Sampler: 88%|████████▊ | 44/50 [00:14<00:01, 4.81it/s] DDIM Sampler: 90%|█████████ | 45/50 [00:14<00:00, 5.01it/s] DDIM Sampler: 92%|█████████▏| 46/50 [00:14<00:00, 5.16it/s] DDIM Sampler: 94%|█████████▍| 47/50 [00:15<00:00, 5.28it/s] DDIM Sampler: 96%|█████████▌| 48/50 [00:15<00:00, 5.36it/s] DDIM Sampler: 98%|█████████▊| 49/50 [00:15<00:00, 5.42it/s] DDIM Sampler: 100%|██████████| 50/50 [00:15<00:00, 5.46it/s] DDIM Sampler: 100%|██████████| 50/50 [00:15<00:00, 3.19it/s]