fasr 0.5.2

FASR: Fast Automatic Speech Recognition Pipeline

fasr

fasr is a production-ready Python speech inference framework built around a composable AudioPipeline.

It lets you combine VAD, ASR, punctuation restoration, language identification, and custom components for offline transcription, batch jobs, and online services.

📖 Chinese README: README_ZH.md

Key Features

• Plugin-based model ecosystem with install-on-demand model packages.
• Unified data structures and component interfaces for easier composition.
• Asynchronous component execution for better CPU / GPU utilization.
• Production-oriented pipeline design for batch, streaming, and service use.

Quick Start

Install the plugins you need first:

pip install fasr-vad-fsmn fasr-asr-paraformer fasr-punc-ct-transformer

Build a pipeline and run inference:

from fasr import AudioPipeline

asr = (
    AudioPipeline()
    .add_pipe("detector", model="fsmn")
    .add_pipe("recognizer", model="paraformer")
    .add_pipe("sentencizer", model="ct_transformer")
)

audios = asr.run(["1.wav", "2.wav", "3.wav"])
for audio in audios:
    print(audio.text)
    # if your audio has multiple channels, you can use audio.channels to get the text of each channel
    for channel in audio.channels:
        print(channel.text)

Supported Models

fasr discovers models through plugin packages. The following models are currently supported in this repository:

Capability	Registered model IDs	Plugin packages
VAD	marblenet, fsmn, fsmn_online, firered, stream_silero	fasr-vad-marblenet, fasr-vad-fsmn, fasr-vad-firered, fasr-vad-silero
ASR	qwen3asr, qwen3_0_6b, qwen3_1_7b, stream_qwen3_0_6b, stream_qwen3_1_7b, paraformer, seaco_paraformer, paraformer_online, firered_aed, firered_llm, fun_asr_nano	fasr-asr-qwen3asr, fasr-asr-paraformer, fasr-asr-firered, fasr-asr-funasr
Punctuation	ct_transformer	fasr-punc-ct-transformer
LID	firered	fasr-lid-firered

Service Deployment

FastAPI service support is provided by the optional fasr-service-fastapi plugin. Install the service extra and start the service:

pip install fasr-service-fastapi
fasr init --cfg config.cfg
fasr serve --cfg config.cfg

You can also inspect the generated config:

fasr serve --print_default_config true
fasr serve --write_default_config run.cfg

Service routers are config-driven. If [service.transcribe_router] is omitted, batch endpoints such as POST /transcribe and POST /inference are not mounted. If [service.realtime_router] is omitted, realtime websocket endpoints are not mounted. At least one router must be configured.

Model Plugin Guide

Model plugin implementation details live in docs/model-plugins.md.