Skip to main content

Leopard Speech-to-Text Engine.

Project description

Leopard Binding for Python

Leopard Speech-to-Text Engine

Made in Vancouver, Canada by Picovoice

Leopard is an on-device speech-to-text engine. Leopard is:

  • Private; All voice processing runs locally.
  • Accurate
  • Compact and Computationally-Efficient
  • Cross-Platform:
    • Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64)
    • Android and iOS
    • Chrome, Safari, Firefox, and Edge
    • Raspberry Pi (3, 4, 5)

Compatibility

  • Python 3.9+
  • Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

Installation

pip3 install pvleopard

AccessKey

Leopard requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Leopard SDKs. You can get your AccessKey for free. Make sure to keep your AccessKey secret. Signup or Login to Picovoice Console to get your AccessKey.

Usage

Create an instance of the engine and transcribe an audio file:

import pvleopard

leopard = pvleopard.create(access_key='${ACCESS_KEY}')

transcript, words = leopard.process_file('${AUDIO_FILE_PATH}')
print(transcript)
for word in words:
    print(
      "{word=\"%s\" start_sec=%.2f end_sec=%.2f confidence=%.2f speaker_tag=%d}"
      % (word.word, word.start_sec, word.end_sec, word.confidence, word.speaker_tag))

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console and ${AUDIO_FILE_PATH} to the path an audio file.

Finally, when done be sure to explicitly release the resources:

leopard.delete()

Language Model

The Leopard Python SDK comes preloaded with a default English language model (.pv file). Default models for other supported languages can be found in lib/common.

Create custom language models using the Picovoice Console. Here you can train language models with custom vocabulary and boost words in the existing vocabulary.

Pass in the .pv file via the model_path argument:

leopard = pvleopard.create(
    access_key='${ACCESS_KEY}',
    model_path='${MODEL_FILE_PATH}')

Word Metadata

Along with the transcript, Leopard returns metadata for each transcribed word. Available metadata items are:

  • Start Time: Indicates when the word started in the transcribed audio. Value is in seconds.
  • End Time: Indicates when the word ended in the transcribed audio. Value is in seconds.
  • Confidence: Leopard's confidence that the transcribed word is accurate. It is a number within [0, 1].
  • Speaker Tag: If speaker diarization is enabled on initialization, the speaker tag is a non-negative integer identifying unique speakers, with 0 reserved for unknown speakers. If speaker diarization is not enabled, the value will always be -1.

Demos

pvleoparddemo provides command-line utilities for processing audio using Leopard.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pvleopard-2.0.5.tar.gz (42.3 MB view details)

Uploaded Source

Built Distribution

pvleopard-2.0.5-py3-none-any.whl (42.3 MB view details)

Uploaded Python 3

File details

Details for the file pvleopard-2.0.5.tar.gz.

File metadata

  • Download URL: pvleopard-2.0.5.tar.gz
  • Upload date:
  • Size: 42.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for pvleopard-2.0.5.tar.gz
Algorithm Hash digest
SHA256 18f518caeb53556aef9dd46059b3013648db85baaa2aa4cc27cc1809e486aac9
MD5 9a4fd70912acd8997c2ccaaa4d847e21
BLAKE2b-256 162d41a45a20accf23206fcf04aca11ddad5bf3df3e02540ea0faa18aa200e74

See more details on using hashes here.

File details

Details for the file pvleopard-2.0.5-py3-none-any.whl.

File metadata

  • Download URL: pvleopard-2.0.5-py3-none-any.whl
  • Upload date:
  • Size: 42.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for pvleopard-2.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 df22546ca81329b5f856363836ad943381ba9a24d1bf0d6904fccba91ed35229
MD5 8e882a1bc3ec039b361556391917a175
BLAKE2b-256 a6bbb01ed94f314a32a27a8b7316acdf385a987176890261362bd764f80a256b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page