miya-speechless
UV Installation Instructions
Step 1: Install uv
You can install uv by running the official installation script:
curl -LsSf https://astral.sh/uv/install.sh | sh
Alternatively, you can install it via Homebrew:
brew install astral-sh/uv/uv
Step 2: Verify uv Installation
After installing, verify that uv is available by running:
uv --version
Step 3: Install Dependencies
Once uv is installed, you can install the project dependencies by running the following command in the project root. Using the --dev flag will install the development dependencies (e.g. pytest, black, isort, etc.).
uv venv
source .venv/bin/activate
uv sync --dev
This will create a virtual environment and install all dependencies specified in the pyproject.toml file.
Step 4: Run the Project or Tests
You can now run the project or the tests using the uv environment:
To activate the environment:
source .venv/bin/activate
To run the tests:
uv run pytest
Step 5: Convert the model to onnx format
To convert the model to onnx format, run the following command:
uv run python export_to_onnx.py --checkpoint /path/to/checkpoint --onnx_model /path/to/onnx_model
Step 6: Run the streamlit app
Place the onnx model in the models directory.
To start the streamlit app, run the following command:
uv run streamlit run app/app.py
To start debug mode, run:
uv run python -m debugpy --listen 5678 -m streamlit run app/app.py
In the application, you can set the following parameters:
- overlap: Set the overlap between the transcript and the diaretization (default: 0.1)
- onset_threshold: Set the onset threshold for speaker start (default: 0.1)
- offset_threshold: Set the offset threshold for speaker stop (default: 0.1)
Step 7: Add OPENAI_API_KEY and/or setup WHISPER_CPP_MODEL
whisper_1 requires a paid OpenAI subscription. An alternative is whisper.cpp
Download at least one supported model:
# Linux
docker run -it --rm -v ./data/models:/models ghcr.io/ggerganov/whisper.cpp:main "./models/download-ggml-model.sh small /models"
# Windows
docker run -it --rm -v "$(pwd -W)/models":/models ghcr.io/ggerganov/whisper.cpp:main "./models/download-ggml-model.sh small /models"
When WHISPER_CPP_MODEL is set, expect the following to happen instead of calling OpenAI:
ffmpeg -i data/temp_results/uploaded_audio.mp3 -ar 16000 -ac 1 -c:a pcm_s16le data/audio/output.wav
# Linux
docker run -it --rm -v ./data/models:/models -v ./data/audio:/audios ghcr.io/ggerganov/whisper.cpp:main "./build/bin/whisper-cli -m /models/ggml-small.bin -f /audios/output.wav -ml 16 -oj -l en"
# Windows
docker run -it --rm -v "$(pwd -W)/data/models":/models -v "$(pwd -W)/data":/audios ghcr.io/ggerganov/whisper.cpp:main "./build/bin/whisper-cli -m /models/ggml-small.bin -f /audios/output.wav -ml 16 -oj -l en"
Metadata
Release files for miya-speechless 0.0.28
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| miya_speechless-0.0.28.tar.gz | 179.9 kB | Without provenance | Details for miya_speechless-0.0.28.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| miya_speechless-0.0.28-py3-none-any.whl | Python 3 | none | any | Details for miya_speechless-0.0.28-py3-none-any.whl |
Total release size: 204.2 kB
Release files / miya_speechless-0.0.28.tar.gz
| Download URL | miya_speechless-0.0.28.tar.gz |
|---|---|
| Size | 179.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
6e3d943c7899dbde3f4532d3777e8e3afee1d2971b462a31c3dfa88e9f48bbd3
|
|
BLAKE2b-256 checksum How to use checksums |
06e76f8becb178666a1c0dab6407bc17e452183ddc26b02e7c6414cab8072374
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.8.15
|
Release files / miya_speechless-0.0.28-py3-none-any.whl
| Download URL | miya_speechless-0.0.28-py3-none-any.whl |
|---|---|
| Size | 24.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a21b6441b90d122028a068a69cf797646541f67384d90c88f24338533167f224
|
|
BLAKE2b-256 checksum How to use checksums |
93689801d55b5a4f8d2ea2e34c00b94ac62b2b70d185bf67cdb3ec89c70b5d4a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.8.15
|
Security for miya-speechless 0.0.28
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.