isaacgv/cog-whisper:38e50c70 – Run with an API on Replicate

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isaacgv /cog-whisper:38e50c70

Playground API

Input

audio

*file

Audio file

model

string

Choose a Whisper model.

Default: "large-v2"

transcription

string

Choose the format for the transcription

Default: "plain text"

translate

boolean

Translate the text to English when set to True

Default: false

language

string

language spoken in the audio, specify None to perform language detection

temperature

number

temperature to use for sampling

Default: 0

patience

number

optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search

suppress_tokens

string

Shift + Return to add a new line

comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations

Default: "-1"

initial_prompt

string

Shift + Return to add a new line

optional text to provide as a prompt for the first window.

condition_on_previous_text

boolean

if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop

Default: true

temperature_increment_on_fallback

number

temperature to increase when falling back when the decoding fails to meet either of the thresholds below

Default: 0.2

compression_ratio_threshold

number

if the gzip compression ratio is higher than this value, treat the decoding as failed

Default: 2.4

logprob_threshold

number

if the average log probability is lower than this value, treat the decoding as failed

Default: -1

no_speech_threshold

number

if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence

Default: 0.6

word_timestamps

boolean

Improves the accuracy of the timestamps by using word-level timestamps

Default: true

Run this model in Node.js with one line of code:

npx create-replicate --model=isaacgv/cog-whisper

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

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

Run isaacgv/cog-whisper using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "isaacgv/cog-whisper:38e50c707a339495ed236b742ef57e2ab4a583fd4e95d906dddef42f40d18ed8",
  {
    input: {
      model: "large-v2",
      language: "af",
      translate: false,
      temperature: 0,
      transcription: "plain text",
      suppress_tokens: "-1",
      word_timestamps: true,
      logprob_threshold: -1,
      no_speech_threshold: 0.6,
      condition_on_previous_text: true,
      compression_ratio_threshold: 2.4,
      temperature_increment_on_fallback: 0.2
    }
  }
);

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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run isaacgv/cog-whisper using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "isaacgv/cog-whisper:38e50c707a339495ed236b742ef57e2ab4a583fd4e95d906dddef42f40d18ed8",
    input={
        "model": "large-v2",
        "language": "af",
        "translate": False,
        "temperature": 0,
        "transcription": "plain text",
        "suppress_tokens": "-1",
        "word_timestamps": True,
        "logprob_threshold": -1,
        "no_speech_threshold": 0.6,
        "condition_on_previous_text": True,
        "compression_ratio_threshold": 2.4,
        "temperature_increment_on_fallback": 0.2
    }
)
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=<paste-your-token-here>

Find your API token in your account settings.

Run isaacgv/cog-whisper 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": "38e50c707a339495ed236b742ef57e2ab4a583fd4e95d906dddef42f40d18ed8",
    "input": {
      "model": "large-v2",
      "language": "af",
      "translate": false,
      "temperature": 0,
      "transcription": "plain text",
      "suppress_tokens": "-1",
      "word_timestamps": true,
      "logprob_threshold": -1,
      "no_speech_threshold": 0.6,
      "condition_on_previous_text": true,
      "compression_ratio_threshold": 2.4,
      "temperature_increment_on_fallback": 0.2
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/isaacgv/cog-whisper@sha256:38e50c707a339495ed236b742ef57e2ab4a583fd4e95d906dddef42f40d18ed8 \
  -i 'model="large-v2"' \
  -i 'language="af"' \
  -i 'translate=false' \
  -i 'temperature=0' \
  -i 'transcription="plain text"' \
  -i 'suppress_tokens="-1"' \
  -i 'word_timestamps=true' \
  -i 'logprob_threshold=-1' \
  -i 'no_speech_threshold=0.6' \
  -i 'condition_on_previous_text=true' \
  -i 'compression_ratio_threshold=2.4' \
  -i 'temperature_increment_on_fallback=0.2'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/isaacgv/cog-whisper@sha256:38e50c707a339495ed236b742ef57e2ab4a583fd4e95d906dddef42f40d18ed8
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "model": "large-v2",
      "language": "af",
      "translate": false,
      "temperature": 0,
      "transcription": "plain text",
      "suppress_tokens": "-1",
      "word_timestamps": true,
      "logprob_threshold": -1,
      "no_speech_threshold": 0.6,
      "condition_on_previous_text": true,
      "compression_ratio_threshold": 2.4,
      "temperature_increment_on_fallback": 0.2
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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