WhisperDRZ is a speaker-aware automatic speech recognition model. It transcribes audio into text with word-level timestamps, per-line speaker tags, and non-speech event tags ([laugh], [breath], ...). It is a Whisper-style encoder-decoder model and handles long audio by chunking and stitching internally.
writeup.mdpip install git+https://github.com/fluxions-ai/whisperdrz
Requires Python 3.12+ and FFmpeg. Runs on a CUDA GPU or on CPU. Weights download automatically from this repo on first use.
whisperdrz audio.wav # defaults: this model, --lang en
whisperdrz audio.wav --output_format json > out.json
whisperdrz audio.wav --lang auto # auto-detect language
import whisperdrz
from whisperdrz.audio import load_audio, SAMPLE_RATE
transcriber = whisperdrz.load_model("whisperdrz-large-v3.safetensors", lang="en")
audio, _ = load_audio("audio.wav", sample_rate=SAMPLE_RATE)
result = transcriber.transcribe(audio.mean(0)) # mono, 16 kHz
print(result.text) # speaker-tagged text with timestamps
print(result.segments) # list of {speaker, start, end, text}
You can also point load_model at this repo id (fluxions/whisperdrz) directly.
Each line begins with a speaker tag. Timed words and tags are wrapped in a start/end timestamp pair; the first and last word of each line are always timed:
[0] <|0.00|>Hello<|0.45|> there <|0.80|>world.<|1.10|>
[1] <|1.20|>Hi<|1.40|> <|1.45|>[laugh]<|1.60|> <|1.70|>there.<|1.95|>
[0], [1], ... are speaker IDs; [c] marks crowd/ambient.<|t|> are timestamps in seconds (two decimals), always in a pair wrapping a word or tag.[laugh], [breath], and similar are non-speech event tags.Measured on this checkpoint:
| Benchmark | WER | DER (miss / FA / conf) | cpWER / tcpWER |
|---|---|---|---|
| ESB (English ASR, 1000 utts) | 9.6% macro / 5.9% micro | — | — |
| Internal conversational (26 clips) | 11.1% | — (WDER 33%) | 46% cpWER |
| VoxConverse dev (216, overlap-heavy) | — | 26.3% (3.1 / 15.8 / 7.5) | — |
| CALLHOME eng (140, 2-spk telephone) | — | 38.6% (5.9 / 14.0 / 18.7) | — |
| AMI test (16 meetings, Mix-Headset) | 22.8% | 50.6% (10.6 / 28.9 / 11.1) | 72% / 84% |
DER is at the standard 0.25s collar, scoring overlapping speech. AMI cpWER/tcpWER and the 22.8% WER use oracle speaker count.
WhisperDRZ is an ASR-first model. Transcription is strong across the board, but diarization trails purpose-built systems (~10–25% DER) — speaker confusion dominates on long, overlapping multi-party audio.
No VAD. It transcribes over silence and music, so false alarm is a large part of DER. Gating to detected speech before scoring recovers much of it:
| Benchmark | DER (ungated) | + silero VAD | + oracle VAD |
|---|---|---|---|
| VoxConverse dev | 26.3% | 24.0% | 20.9% |
| CALLHOME eng | 38.6% | 34.2% | 28.6% |
| AMI test | 50.6% | 40.6% | 31.1% |
See the write-up in the code repo for full analysis (timing, non-speech events, multilingual).
MIT.
WhisperDRZ is a speaker-aware automatic speech recognition model. It transcribes audio into text with word-level timestamps, per-line speaker tags, and non-speech event tags ([laugh], [breath], ...). It is a Whisper-style encoder-decoder model and handles long audio by chunking and stitching internally.
writeup.mdpip install git+https://github.com/fluxions-ai/whisperdrz
Requires Python 3.12+ and FFmpeg. Runs on a CUDA GPU or on CPU. Weights download automatically from this repo on first use.
whisperdrz audio.wav # defaults: this model, --lang en
whisperdrz audio.wav --output_format json > out.json
whisperdrz audio.wav --lang auto # auto-detect language
import whisperdrz
from whisperdrz.audio import load_audio, SAMPLE_RATE
transcriber = whisperdrz.load_model("whisperdrz-large-v3.safetensors", lang="en")
audio, _ = load_audio("audio.wav", sample_rate=SAMPLE_RATE)
result = transcriber.transcribe(audio.mean(0)) # mono, 16 kHz
print(result.text) # speaker-tagged text with timestamps
print(result.segments) # list of {speaker, start, end, text}
You can also point load_model at this repo id (fluxions/whisperdrz) directly.
Each line begins with a speaker tag. Timed words and tags are wrapped in a start/end timestamp pair; the first and last word of each line are always timed:
[0] <|0.00|>Hello<|0.45|> there <|0.80|>world.<|1.10|>
[1] <|1.20|>Hi<|1.40|> <|1.45|>[laugh]<|1.60|> <|1.70|>there.<|1.95|>
[0], [1], ... are speaker IDs; [c] marks crowd/ambient.<|t|> are timestamps in seconds (two decimals), always in a pair wrapping a word or tag.[laugh], [breath], and similar are non-speech event tags.Measured on this checkpoint:
| Benchmark | WER | DER (miss / FA / conf) | cpWER / tcpWER |
|---|---|---|---|
| ESB (English ASR, 1000 utts) | 9.6% macro / 5.9% micro | — | — |
| Internal conversational (26 clips) | 11.1% | — (WDER 33%) | 46% cpWER |
| VoxConverse dev (216, overlap-heavy) | — | 26.3% (3.1 / 15.8 / 7.5) | — |
| CALLHOME eng (140, 2-spk telephone) | — | 38.6% (5.9 / 14.0 / 18.7) | — |
| AMI test (16 meetings, Mix-Headset) | 22.8% | 50.6% (10.6 / 28.9 / 11.1) | 72% / 84% |
DER is at the standard 0.25s collar, scoring overlapping speech. AMI cpWER/tcpWER and the 22.8% WER use oracle speaker count.
WhisperDRZ is an ASR-first model. Transcription is strong across the board, but diarization trails purpose-built systems (~10–25% DER) — speaker confusion dominates on long, overlapping multi-party audio.
No VAD. It transcribes over silence and music, so false alarm is a large part of DER. Gating to detected speech before scoring recovers much of it:
| Benchmark | DER (ungated) | + silero VAD | + oracle VAD |
|---|---|---|---|
| VoxConverse dev | 26.3% | 24.0% | 20.9% |
| CALLHOME eng | 38.6% | 34.2% | 28.6% |
| AMI test | 50.6% | 40.6% | 31.1% |
See the write-up in the code repo for full analysis (timing, non-speech events, multilingual).
MIT.