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Modern speech recognition with word-level timestamps and speaker diarization. Fork of WhisperX with torch 2.6+, pyannote 4.x compatibility.

Project description

MurmurAI

Modern speech recognition with word-level timestamps and speaker diarization

PyPI CI Python License


MurmurAI is a fork of WhisperX with modern dependency support:

  • PyTorch 2.6+ compatibility (weights_only patches)
  • Pyannote 4.x support (token parameter migration)
  • Torchaudio 2.9+ compatibility (audio backend fixes)
  • Python 3.10-3.13 tested

Features

  • Word-level timestamps via phoneme alignment
  • Speaker diarization with pyannote.audio
  • Batch inference for 70x realtime transcription
  • VAD preprocessing (pyannote or silero)
  • Multiple output formats: SRT, VTT, TXT, TSV, JSON

Installation

pip install murmurai-core

Or with uv:

uv add murmurai-core

Quick Start

Python API

import murmurai

# Load model
model = murmurai.load_model("large-v3-turbo", device="cuda", compute_type="float16")

# Transcribe
audio = murmurai.load_audio("audio.mp3")
result = model.transcribe(audio, batch_size=16)

# Align (word-level timestamps)
model_a, metadata = murmurai.load_align_model(language_code=result["language"], device="cuda")
result = murmurai.align(result["segments"], model_a, metadata, audio, device="cuda")

# Diarization (speaker labels)
from pyannote.audio import Pipeline
diarize_model = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token="YOUR_HF_TOKEN")
diarize_segments = diarize_model(audio)
result = murmurai.assign_word_speakers(diarize_segments, result)

CLI

murmurai-core audio.mp3 --model large-v3-turbo --diarize --hf_token YOUR_TOKEN

Requirements

  • NVIDIA GPU with CUDA support (or CPU mode)
  • HuggingFace token for diarization models

Accept the license at pyannote/speaker-diarization-3.1 before using diarization.

Migration from WhisperX

# Before
import whisperx

# After
import murmurai  # drop-in replacement

All APIs are identical. Just change the import.

Credits

MurmurAI builds on the excellent work of:


Made with ❤️ by Namastex Labs

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