cockatoo_chain

This repo is intended for STT, LLM and TSS models chaining.

Installation

Use below command to install the cockatoo_chain package:

# Create Python virtual environment
$ python -m venv env

# Enter virtual Python runtime
$ source env/bin/activate

# Install the target package
$ pip install cockatoo_chain

# Validate the installation
$ python -c 'import cockatoo_chain' && echo 'Success'
Success

Development Mode

Development Mode is intended for users who wish to contribute to the repo and thus needs to install additional dev-related packages for, e.g., code quality checking, to satisfy the standard of the repo. If you hope to contribute to the repository, please follow up the follow instructions:

Create virtual environment by poetry

# Force poetry to build virtual environment in the repo.
$ poetry config virtualenvs.in-project true

# Create virtual environment
$ poetry env use python

# Confirm the created virtual environment
$ poetry env info

Enter virtual environment and installed packages

# Get the command to enter virtual environment
$ poetry env activate
$ source activate <your venv>
$ make init-repo-setup

Model A Usage

From cockagoo_chain package, you can easily access the power of Model A (Speech-to-text) with a few lines of codes. We will learn how to from this section. For the time being, cockagoo_chain support below types of model A:

Get supported Model A options

You can use below code snippet to get the supported model A options:

>>> from cockatoo_chain.utils import model_a
>>> model_type = model_a.ModelType
>>> list(model_type)
[<ModelType.OPEN_AI_WHISPER: 'open_ai_whisper'>]

Transform input audio file into text

Below code snippet demonstrates how to obtain the OpenAI whipser wrapper for model A and apply it to transform the audio file into text:

>>> test_en_audio_file_path = '~/test_audio_files/en_20240108_johnlee.wav'
>>> model_a_wrapper = model_a.get(model_type.OPEN_AI_WHISPER)
>>> response = model_a_wrapper.audio_2_text(test_en_audio_file_path)
>>> response
Audio2TextData(
    text='Hello, this is for testing in English. We will use this to evaluate model SST...',
    spent_time_sec=7.87896990776062,
    audio_file_path='/root/test_audio_files/en_20240108_johnlee.wav')
>>> response.text
'Hello, this is for testing in English. We will use this to evaluate model SST and see how it performs. Thanks.'

Model C Usage

From cockagoo_chain package, you can easily access the power of Model C (Text-to-speech) with a few lines of codes. We will learn how to from this section. For the time being, cockagoo_chain support below types of model C:

Get supported Model C options

>>> from cockatoo_chain.utils import model_c
>>> mt = model_c.ModelType
>>> list(mt)
[<ModelType.GCP_TEXT_2_SPEECH: 'gcp_text_2_speech'>]

Transform text into speech

Below code snippet demonstrates how to obtain the GCP text-to-speech wrapper as model C and apply it to transform give text into speech and save it as audio file:

>>> mc_imp = model_c.get(mt.GCP_TEXT_2_SPEECH)
>>> mc_imp.name
'GCP/text-to-speech'
>>> mc_imp.text_2_audio("Nice to meet you all and welcome to join Cockatoo-AI's group meeting!")
Text2AudioData(
    text="Nice to meet you all and welcome to join Cockatoo-AI's group meeting!",
    spent_time_sec=1.822951,
    generated_audio_file_path='/tmp/gcp_tts_output.wav')