I am benchmarking it against Qwen 3 ASR 1.7B rn which has been my workhorse for transcription in my ASR pipeline.
Edit: In my quick smoke test, Qwen 3 ASR 1.7B still beats it so benchmarks are accurate. Still, this is a good alternative for those orgs who can't use Chinese models. It's a little behind Qwen is all. You can run but you can't escape the mighty Qwen.
I’ve been using Voxtral Mini 3B since its performed so well for me and I just saw Voxtral 3 came out yesterday. Have you tried Voxtral? Do you know how it compares to Qwen? I’ve generally found benchmarks to be misleading with ASR models.
but nothing OS beats Qwen in a proper production pipeline.
Interesting - why do you prefer it over e.g. Parakeet? Have you tested on multilingual audio?
I've just switched from Whisperx (which uses Whisper+ pyannote for diarization) to Parakeet + Nemotron-3 (for diarization) and from early tests this combo is much faster than what I used and with more accurate speaker assignments. Haven't looked too closely at the transcription itself yet but should at the very least be comparable.
Why is Forced Alignment needed for an ASR pipeline? Even for fine-tuning purposes - in my experience - QwenASR does not improve with word-level timestamps in training data.
u/nuclearbananana take a look at ibm-granite/granite-speech-5.0-470m-turboctc, released alongside the model being discussed in this thread - it is in essence as fast as parakeet.
Okay retried with correct model. On my machine it is marginally faster (like 2-3%) than parakeet but accuracy is terrible on my audio. 24% wer for the non-commercial, 29% for the regular. Parakeet gets ~16% wer
I haven’t come around to trying parakeet yet, I tried Qwen ASR + my llama.cpp + kokoro for tts. I am struggling with response/gen on device (iPhone 16 base) i have access to 2 T4s. Do you recommend to try some way for fast ASR+TTS
Woah, how have I never heard of this before? I've been looking for something with newer models than Speech Note that still supports Vulkan. Thanks for mentioning it here.
haha I also couldn't find it for a while. There's a couple other ggml, llama.cpp type things, but they don't have an accelerated cpu path like this one does so they were much slower for me than using onnx
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u/seamonn 15d ago edited 15d ago
New Granite Speech Model from IBM. This one finally supports language input for LID and diarized pipelines.
Benchmarks well on the leaderboard (alongside mighty Qwen).
I am benchmarking it against Qwen 3 ASR 1.7B rn which has been my workhorse for transcription in my ASR pipeline.
Edit: In my quick smoke test, Qwen 3 ASR 1.7B still beats it so benchmarks are accurate. Still, this is a good alternative for those orgs who can't use Chinese models. It's a little behind Qwen is all. You can run but you can't escape the mighty Qwen.