Prediction

Model

suno-ai/bark:b76242b4

ID

2h3xkrjfxnfupepyvcp5mfzvhm

Status

Succeeded

Source

Web

Hardware

–

Total duration

6m 21s

Created

over 2 years ago

Webhook

–

Input

prompt: Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.
history_prompt: en_speaker_1
text_temp: 0.7
waveform_temp: 0.7

{
  "history_prompt": "en_speaker_1",
  "prompt": "Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.",
  "text_temp": 0.7,
  "waveform_temp": 0.7
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_f0Q**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";

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

Run suno-ai/bark using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "suno-ai/bark:b76242b40d67c76ab6742e987628a2a9ac019e11d56ab96c4e91ce03b79b2787",
  {
    input: {
      history_prompt: "en_speaker_1",
      prompt: "Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.",
      text_temp: 0.7,
      waveform_temp: 0.7
    }
  }
);

console.log(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_f0Q**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run suno-ai/bark using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "suno-ai/bark:b76242b40d67c76ab6742e987628a2a9ac019e11d56ab96c4e91ce03b79b2787",
    input={
        "history_prompt": "en_speaker_1",
        "prompt": "Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.",
        "text_temp": 0.7,
        "waveform_temp": 0.7
    }
)

print(output)

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_f0Q**********************************

This is your API token. Keep it to yourself.

Run suno-ai/bark 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": "suno-ai/bark:b76242b40d67c76ab6742e987628a2a9ac019e11d56ab96c4e91ce03b79b2787",
    "input": {
      "history_prompt": "en_speaker_1",
      "prompt": "Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.",
      "text_temp": 0.7,
      "waveform_temp": 0.7
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Object output with 1 properties

audio_out(audio.wav)

Download output

{
  "id": "2h3xkrjfxnfupepyvcp5mfzvhm",
  "model": "suno-ai/bark",
  "version": "b76242b40d67c76ab6742e987628a2a9ac019e11d56ab96c4e91ce03b79b2787",
  "input": {
    "history_prompt": "en_speaker_1",
    "prompt": "Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.",
    "text_temp": 0.7,
    "waveform_temp": 0.7
  },
  "logs": "0%|          | 0/100 [00:00<?, ?it/s]\n  1%|          | 1/100 [00:00<00:14,  7.04it/s]\n  3%|▎         | 3/100 [00:00<00:10,  9.57it/s]\n  5%|▌         | 5/100 [00:00<00:09, 10.52it/s]\n  7%|▋         | 7/100 [00:00<00:08, 11.18it/s]\n  9%|▉         | 9/100 [00:00<00:08, 11.08it/s]\n 11%|█         | 11/100 [00:01<00:08, 11.05it/s]\n 13%|█▎        | 13/100 [00:01<00:07, 11.10it/s]\n 15%|█▌        | 15/100 [00:01<00:07, 11.29it/s]\n 17%|█▋        | 17/100 [00:01<00:07, 11.49it/s]\n 19%|█▉        | 19/100 [00:01<00:07, 11.39it/s]\n 21%|██        | 21/100 [00:01<00:07, 11.24it/s]\n 23%|██▎       | 23/100 [00:02<00:06, 11.08it/s]\n 25%|██▌       | 25/100 [00:02<00:06, 10.96it/s]\n 27%|██▋       | 27/100 [00:02<00:06, 11.43it/s]\n 29%|██▉       | 29/100 [00:02<00:06, 11.59it/s]\n 31%|███       | 31/100 [00:02<00:05, 11.58it/s]\n 33%|███▎      | 33/100 [00:02<00:05, 11.69it/s]\n 35%|███▌      | 35/100 [00:03<00:05, 11.76it/s]\n 37%|███▋      | 37/100 [00:03<00:05, 11.53it/s]\n 39%|███▉      | 39/100 [00:03<00:05, 11.79it/s]\n 41%|████      | 41/100 [00:03<00:05, 11.71it/s]\n 43%|████▎     | 43/100 [00:03<00:04, 11.92it/s]\n 45%|████▌     | 45/100 [00:03<00:04, 11.92it/s]\n 47%|████▋     | 47/100 [00:04<00:04, 11.52it/s]\n 49%|████▉     | 49/100 [00:04<00:04, 11.38it/s]\n 51%|█████     | 51/100 [00:04<00:04, 11.64it/s]\n 53%|█████▎    | 53/100 [00:04<00:04, 11.43it/s]\n 55%|█████▌    | 55/100 [00:04<00:03, 11.62it/s]\n 57%|█████▋    | 57/100 [00:05<00:03, 11.52it/s]\n 59%|█████▉    | 59/100 [00:05<00:03, 11.24it/s]\n 61%|██████    | 61/100 [00:05<00:03, 11.35it/s]\n 63%|██████▎   | 63/100 [00:05<00:03, 11.27it/s]\n 65%|██████▌   | 65/100 [00:05<00:03, 11.36it/s]\n 67%|██████▋   | 67/100 [00:05<00:02, 11.41it/s]\n 69%|██████▉   | 69/100 [00:06<00:02, 10.82it/s]\n 71%|███████   | 71/100 [00:06<00:02, 10.96it/s]\n 73%|███████▎  | 73/100 [00:06<00:02, 11.06it/s]\n 75%|███████▌  | 75/100 [00:06<00:02, 10.88it/s]\n 77%|███████▋  | 77/100 [00:06<00:02, 10.95it/s]\n 79%|███████▉  | 79/100 [00:07<00:01, 10.80it/s]\n 81%|████████  | 81/100 [00:07<00:01, 10.64it/s]\n 83%|████████▎ | 83/100 [00:07<00:01, 10.73it/s]\n100%|██████████| 100/100 [00:07<00:00, 39.52it/s]\n100%|██████████| 100/100 [00:07<00:00, 13.31it/s]\n  0%|          | 0/33 [00:00<?, ?it/s]\n  3%|▎         | 1/33 [00:00<00:28,  1.10it/s]\n  6%|▌         | 2/33 [00:01<00:27,  1.11it/s]\n  9%|▉         | 3/33 [00:02<00:27,  1.11it/s]\n 12%|█▏        | 4/33 [00:03<00:26,  1.11it/s]\n 15%|█▌        | 5/33 [00:04<00:25,  1.12it/s]\n 18%|█▊        | 6/33 [00:05<00:24,  1.11it/s]\n 21%|██        | 7/33 [00:06<00:23,  1.12it/s]\n 24%|██▍       | 8/33 [00:07<00:22,  1.12it/s]\n 27%|██▋       | 9/33 [00:08<00:21,  1.12it/s]\n 30%|███       | 10/33 [00:08<00:20,  1.12it/s]\n 33%|███▎      | 11/33 [00:09<00:19,  1.12it/s]\n 36%|███▋      | 12/33 [00:10<00:18,  1.12it/s]\n 39%|███▉      | 13/33 [00:11<00:17,  1.12it/s]\n 42%|████▏     | 14/33 [00:12<00:16,  1.12it/s]\n 45%|████▌     | 15/33 [00:13<00:16,  1.12it/s]\n 48%|████▊     | 16/33 [00:14<00:15,  1.12it/s]\n 52%|█████▏    | 17/33 [00:15<00:14,  1.12it/s]\n 55%|█████▍    | 18/33 [00:16<00:13,  1.12it/s]\n 58%|█████▊    | 19/33 [00:16<00:12,  1.12it/s]\n 61%|██████    | 20/33 [00:17<00:11,  1.11it/s]\n 64%|██████▎   | 21/33 [00:18<00:10,  1.11it/s]\n 67%|██████▋   | 22/33 [00:19<00:09,  1.11it/s]\n 70%|██████▉   | 23/33 [00:20<00:09,  1.11it/s]\n 73%|███████▎  | 24/33 [00:21<00:08,  1.11it/s]\n 76%|███████▌  | 25/33 [00:22<00:07,  1.11it/s]\n 79%|███████▉  | 26/33 [00:23<00:06,  1.11it/s]\n 82%|████████▏ | 27/33 [00:24<00:05,  1.11it/s]\n 85%|████████▍ | 28/33 [00:25<00:04,  1.11it/s]\n 88%|████████▊ | 29/33 [00:26<00:03,  1.11it/s]\n 91%|█████████ | 30/33 [00:26<00:02,  1.10it/s]\n 94%|█████████▍| 31/33 [00:27<00:01,  1.10it/s]\n 97%|█████████▋| 32/33 [00:28<00:00,  1.10it/s]\n100%|██████████| 33/33 [00:29<00:00,  1.37it/s]\n100%|██████████| 33/33 [00:29<00:00,  1.14it/s]",
  "output": {
    "audio_out": "https://replicate.delivery/pbxt/MnaUCnn7YQKqLZ5zoQ6es0WrD6PpILn7RObALqeyElOiqI2QA/audio.wav"
  },
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-04-27T20:12:22.077829Z",
  "started_at": "2023-04-27T20:18:02.61119Z",
  "completed_at": "2023-04-27T20:18:43.417209Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/2h3xkrjfxnfupepyvcp5mfzvhm/cancel",
    "get": "https://api.replicate.com/v1/predictions/2h3xkrjfxnfupepyvcp5mfzvhm"
  },
  "metrics": {
    "predict_time": 40.806019,
    "total_time": 381.33938
  }
}

Generated in

40.8 seconds

Tweak it Report

0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:14, 7.04it/s] 3%|▎ | 3/100 [00:00<00:10, 9.57it/s] 5%|▌ | 5/100 [00:00<00:09, 10.52it/s] 7%|▋ | 7/100 [00:00<00:08, 11.18it/s] 9%|▉ | 9/100 [00:00<00:08, 11.08it/s] 11%|█ | 11/100 [00:01<00:08, 11.05it/s] 13%|█▎ | 13/100 [00:01<00:07, 11.10it/s] 15%|█▌ | 15/100 [00:01<00:07, 11.29it/s] 17%|█▋ | 17/100 [00:01<00:07, 11.49it/s] 19%|█▉ | 19/100 [00:01<00:07, 11.39it/s] 21%|██ | 21/100 [00:01<00:07, 11.24it/s] 23%|██▎ | 23/100 [00:02<00:06, 11.08it/s] 25%|██▌ | 25/100 [00:02<00:06, 10.96it/s] 27%|██▋ | 27/100 [00:02<00:06, 11.43it/s] 29%|██▉ | 29/100 [00:02<00:06, 11.59it/s] 31%|███ | 31/100 [00:02<00:05, 11.58it/s] 33%|███▎ | 33/100 [00:02<00:05, 11.69it/s] 35%|███▌ | 35/100 [00:03<00:05, 11.76it/s] 37%|███▋ | 37/100 [00:03<00:05, 11.53it/s] 39%|███▉ | 39/100 [00:03<00:05, 11.79it/s] 41%|████ | 41/100 [00:03<00:05, 11.71it/s] 43%|████▎ | 43/100 [00:03<00:04, 11.92it/s] 45%|████▌ | 45/100 [00:03<00:04, 11.92it/s] 47%|████▋ | 47/100 [00:04<00:04, 11.52it/s] 49%|████▉ | 49/100 [00:04<00:04, 11.38it/s] 51%|█████ | 51/100 [00:04<00:04, 11.64it/s] 53%|█████▎ | 53/100 [00:04<00:04, 11.43it/s] 55%|█████▌ | 55/100 [00:04<00:03, 11.62it/s] 57%|█████▋ | 57/100 [00:05<00:03, 11.52it/s] 59%|█████▉ | 59/100 [00:05<00:03, 11.24it/s] 61%|██████ | 61/100 [00:05<00:03, 11.35it/s] 63%|██████▎ | 63/100 [00:05<00:03, 11.27it/s] 65%|██████▌ | 65/100 [00:05<00:03, 11.36it/s] 67%|██████▋ | 67/100 [00:05<00:02, 11.41it/s] 69%|██████▉ | 69/100 [00:06<00:02, 10.82it/s] 71%|███████ | 71/100 [00:06<00:02, 10.96it/s] 73%|███████▎ | 73/100 [00:06<00:02, 11.06it/s] 75%|███████▌ | 75/100 [00:06<00:02, 10.88it/s] 77%|███████▋ | 77/100 [00:06<00:02, 10.95it/s] 79%|███████▉ | 79/100 [00:07<00:01, 10.80it/s] 81%|████████ | 81/100 [00:07<00:01, 10.64it/s] 83%|████████▎ | 83/100 [00:07<00:01, 10.73it/s] 100%|██████████| 100/100 [00:07<00:00, 39.52it/s] 100%|██████████| 100/100 [00:07<00:00, 13.31it/s] 0%| | 0/33 [00:00<?, ?it/s] 3%|▎ | 1/33 [00:00<00:28, 1.10it/s] 6%|▌ | 2/33 [00:01<00:27, 1.11it/s] 9%|▉ | 3/33 [00:02<00:27, 1.11it/s] 12%|█▏ | 4/33 [00:03<00:26, 1.11it/s] 15%|█▌ | 5/33 [00:04<00:25, 1.12it/s] 18%|█▊ | 6/33 [00:05<00:24, 1.11it/s] 21%|██ | 7/33 [00:06<00:23, 1.12it/s] 24%|██▍ | 8/33 [00:07<00:22, 1.12it/s] 27%|██▋ | 9/33 [00:08<00:21, 1.12it/s] 30%|███ | 10/33 [00:08<00:20, 1.12it/s] 33%|███▎ | 11/33 [00:09<00:19, 1.12it/s] 36%|███▋ | 12/33 [00:10<00:18, 1.12it/s] 39%|███▉ | 13/33 [00:11<00:17, 1.12it/s] 42%|████▏ | 14/33 [00:12<00:16, 1.12it/s] 45%|████▌ | 15/33 [00:13<00:16, 1.12it/s] 48%|████▊ | 16/33 [00:14<00:15, 1.12it/s] 52%|█████▏ | 17/33 [00:15<00:14, 1.12it/s] 55%|█████▍ | 18/33 [00:16<00:13, 1.12it/s] 58%|█████▊ | 19/33 [00:16<00:12, 1.12it/s] 61%|██████ | 20/33 [00:17<00:11, 1.11it/s] 64%|██████▎ | 21/33 [00:18<00:10, 1.11it/s] 67%|██████▋ | 22/33 [00:19<00:09, 1.11it/s] 70%|██████▉ | 23/33 [00:20<00:09, 1.11it/s] 73%|███████▎ | 24/33 [00:21<00:08, 1.11it/s] 76%|███████▌ | 25/33 [00:22<00:07, 1.11it/s] 79%|███████▉ | 26/33 [00:23<00:06, 1.11it/s] 82%|████████▏ | 27/33 [00:24<00:05, 1.11it/s] 85%|████████▍ | 28/33 [00:25<00:04, 1.11it/s] 88%|████████▊ | 29/33 [00:26<00:03, 1.11it/s] 91%|█████████ | 30/33 [00:26<00:02, 1.10it/s] 94%|█████████▍| 31/33 [00:27<00:01, 1.10it/s] 97%|█████████▋| 32/33 [00:28<00:00, 1.10it/s] 100%|██████████| 33/33 [00:29<00:00, 1.37it/s] 100%|██████████| 33/33 [00:29<00:00, 1.14it/s]