This project is a real-time transcription application that uses the OpenAI Whisper model to convert speech input into text output. It can be used to transcribe both live audio input from microphone and pre-recorded audio files.
Install PyAudio and ffmpeg
bash scripts/setup.sh
Install whisper-live from pip
pip install whisper-live
The server supports two backends faster_whisper
and tensorrt
. If running tensorrt
backend follow TensorRT_whisper readme
python3 run_server.py --port 9090 \
--backend faster_whisper
python3 run_server.py --port 9090 \ --backend faster_whisper \ -fw "/path/to/custom/faster/whisper/model"
- TensorRT backend. Currently, we recommend to only use the docker setup for TensorRT. Follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) which works as expected. Make sure to build your TensorRT Engines before running the server with TensorRT backend.
```bash
# Run English only model
python3 run_server.py -p 9090 \
-b tensorrt \
-trt /home/TensorRT-LLM/examples/whisper/whisper_small_en
# Run Multilingual model
python3 run_server.py -p 9090 \
-b tensorrt \
-trt /home/TensorRT-LLM/examples/whisper/whisper_small \
-m
To control the number of threads used by OpenMP, you can set the OMP_NUM_THREADS
environment variable. This is useful for managing CPU resources and ensuring consistent performance. If not specified, OMP_NUM_THREADS
is set to 1
by default. You can change this by using the --omp_num_threads
argument:
python3 run_server.py --port 9090 \
--backend faster_whisper \
--omp_num_threads 4
By default, when running the server without specifying a model, the server will instantiate a new whisper model for every client connection. This has the advantage, that the server can use different model sizes, based on the client's requested model size. On the other hand, it also means you have to wait for the model to be loaded upon client connection and you will have increased (V)RAM usage.
When serving a custom TensorRT model using the -trt
or a custom faster_whisper model using the -fw
option, the server will instead only instantiate the custom model once and then reuse it for all client connections.
If you don't want this, set --no_single_model
.
Initializing the client with below parameters:
lang
: Language of the input audio, applicable only if using a multilingual model.translate
: If set to True
then translate from any language to en
.model
: Whisper model size.use_vad
: Whether to use Voice Activity Detection
on the server.save_output_recording
: Set to True to save the microphone input as a .wav
file during live transcription. This option is helpful for recording sessions for later playback or analysis. Defaults to False
. output_recording_filename
: Specifies the .wav
file path where the microphone input will be saved if save_output_recording
is set to True
.
from whisper_live.client import TranscriptionClient
client = TranscriptionClient(
"localhost",
9090,
lang="en",
translate=False,
model="small",
use_vad=False,
save_output_recording=True, # Only used for microphone input, False by Default
output_recording_filename="./output_recording.wav" # Only used for microphone input
)
It connects to the server running on localhost at port 9090. Using a multilingual model, language for the transcription will be automatically detected. You can also use the language option to specify the target language for the transcription, in this case, English ("en"). The translate option should be set to True
if we want to translate from the source language to English and False
if we want to transcribe in the source language.
Transcribe an audio file:
client("tests/jfk.wav")
To transcribe from microphone:
client()
To transcribe from a RTSP stream:
client(rtsp_url="rtsp://admin:admin@192.168.0.1/rtsp")
To transcribe from a HLS stream:
client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/bbc_1xtra.isml/bbc_1xtra-audio%3d96000.norewind.m3u8")
GPU
Faster-Whisper
docker run -it --gpus all -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest
TensorRT.
docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt
bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en
python3 run_server.py --port 9090 \ --backend tensorrt \ --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en"
CPU
docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest
Note: By default we use "small" model size. To build docker image for a different model size, change the size in server.py and then build the docker image.
We are available to help you with both Open Source and proprietary AI projects. You can reach us via the Collabora website or vineet.suryan@collabora.com and marcus.edel@collabora.com.
@article{Whisper
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
url = {https://arxiv.org/abs/2212.04356},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
publisher = {arXiv},
year = {2022},
}
@misc{Silero VAD,
author = {Silero Team},
title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/snakers4/silero-vad}},
email = {hello@silero.ai}
}