whisper-timestamped
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
GraphCanon updated 3w · GitHub synced 3w · 29 views this month
Decision brief
whisper-timestamped is a Python library that extends OpenAI's Whisper model with word-level timestamps and confidence scoring.
Good fit when
- When you need precise timestamping for each spoken word in speech recognition output.
- For multilingual speech processing where accurate time alignment between audio and transcriptions is essential.
Avoid when
- Avoid if your project requires minimal dependencies or a smaller footprint, as it includes several external libraries like matplotlib and transformers.
- Not suitable for real-time applications where low latency is crucial due to the additional overhead of timestamp generation and confidence scoring.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (324d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install whisper-timestampedSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A library for speech recognition that adds fine-grained timestamping and confidence to the output of OpenAI's Whisper model.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 30, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 30, 2026
- Languages
- python
Source: github.language · Jul 30, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 30, 2026)
* `python3` (version higher or equal to 3.7, at least 3.9 is recommended)Source link
Tags
README
First installation
Requirements:
python3(version higher or equal to 3.7, at least 3.9 is recommended)ffmpeg(see instructions for installation on the whisper repository)
You can install whisper-timestamped either by using pip:
pip3 install whisper-timestamped
or by cloning this repository and running installation:
git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
python3 setup.py install
Additional packages that might be needed
If you want to plot alignment between audio timestamps and words (as in this section), you also need matplotlib:
pip3 install matplotlib
If you want to use VAD option (Voice Activity Detection before running Whisper model), you also need torchaudio and onnxruntime:
pip3 install onnxruntime torchaudio
If you want to use finetuned Whisper models from the Hugging Face Hub, you also need transformers:
pip3 install transformers
Docker
A docker image of about 9GB can be built using:
git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
docker build -t whisper_timestamped:latest .
Light installation for CPU
If you don't have a GPU (or don't want to use it), then you don't need to install the CUDA dependencies. You should then just install a light version of torch before installing whisper-timestamped, for instance as follows:
pip3 install \
torch==1.13.1+cpu \
torchaudio==0.13.1+cpu \
-f https://download.pytorch.org/whl/torch_stable.html
A specific docker image of about 3.5GB can also be built using:
git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
docker build -t whisper_timestamped_cpu:latest -f Dockerfile.cpu .
For agents
This page has a .md twin and JSON over the API.