granite speech 5.0 470m turboctc
Granite Speech 5.0 470M TurboCTC is a compact 470 million parameter English automatic speech recognition model released by IBM under the Apache 2.0 license. It uses a conformer acoustic encoder with block self-attention, self-conditioning, and temporal downsampling, with an output layer of 16,384 BPE units. The model was trained on approximately 60,000 hours of English audio from public corpora using Connectionist Temporal Classification (CTC), and inference is non-autoregressive with greedy decoding. It is designed for low-latency and high-throughput English speech-to-text transcription, and is described as suitable for deployment on laptops, smartphones, and other edge devices. Native support is available in transformers>=5.16.0, with additional usage options for Apple Silicon via mlx-audio and local GGUF inference via transcribe.cpp.
At a glance
- Parameters
- 0.47B
- Architecture
- granite_speech5_ctc
- License
- apache-2.0
- Disk space
- 0.9 GB
- Software
- Transformers
Will it run on your machine?
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Add my hardwareBest for
- Low-latency English speech-to-text transcription
- High-throughput English ASR in enterprise applications
- Deployment on laptops, smartphones, and other edge devices
- English transcription using transformers with CTC greedy decoding
- Local inference via quantized GGUF on Metal, Vulkan, CUDA, ROCm, or CPU
Tradeoffs
- Supports English only
- Automatic speech recognition only
- Non-autoregressive CTC greedy decoding without language model rescoring
- No stated support for long-form or streaming audio
- No stated hardware performance, compatibility, or testing details beyond the model card
Ways to run it
Transformers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
from datasets import Audio, load_dataset
from transformers import AutoModelForCTC, AutoProcessor
model_id = "ibm-granite/granite-speech-5.0-470m-turboctc"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCTC.from_pretrained(model_id, device_map="auto")
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el["array"] for el in ds["audio"][:5]]
# `device` computes the log-mel front-end on the model's accelerator, saving a host-to-device copy
inputs = processor(
speech_samples, sampling_rate=processor.feature_extractor.sampling_rate, device=model.device
)
inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs)
print(processor.batch_decode(outputs, skip_special_tokens=True))Explore its uses
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