kb-whisper-tiny

import torch
from datasets import load_dataset
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "KBLab/kb-whisper-tiny"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, use_safetensors=True, cache_dir="cache", revision="strict"
)

import torch
from datasets import load_dataset
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "KBLab/kb-whisper-tiny"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, use_safetensors=True, cache_dir="cache"
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

generate_kwargs = {"task": "transcribe", "language": "sv"}
# Add return_timestamps=True for output with timestamps
res = pipe("audio.mp3", 
           chunk_length_s=30,
           generate_kwargs={"task": "transcribe", "language": "sv"})
print(res)

#### faster-whisper model ####
from faster_whisper import WhisperModel

model_id = "KBLab/kb-whisper-tiny"
model = WhisperModel(
    model_id,
    device="cuda",
    compute_type="float16",
    download_root="cache", # cache directory
    # condition_on_previous_text = False # Can reduce hallucinations if we don't use prompts
)

# Transcribe audio.wav (convert to 16khz mono wav first via ffmpeg)
segments, info = model.transcribe("audio.wav", condition_on_previous_text=False)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

import whisperx

device = "cuda"
audio_file = "audio.wav"
batch_size = 16  # reduce if low on GPU mem
compute_type = "float16"  # change to "int8" if low on GPU mem (may reduce accuracy)

# 1. Transcribe with original whisper (batched)
model = whisperx.load_model(
    "KBLab/kb-whisper-tiny", device, compute_type=compute_type, download_root="cache"  # cache_dir
)

audio = whisperx.load_audio(audio_file)
result = model.transcribe(audio, batch_size=batch_size)
print(result["segments"])  # before alignment

# delete model if low on GPU resources
# import gc; gc.collect(); torch.cuda.empty_cache(); del model

# 2. Align whisper output
model_a, metadata = whisperx.load_align_model(
    language_code=result["language"],
    device=device,
    model_name="KBLab/wav2vec2-large-voxrex-swedish",
    model_dir="cache",  # cache_dir
)
result = whisperx.align(
    result["segments"], model_a, metadata, audio, device, return_char_alignments=False
)

print(result["segments"])  # word level timestamps after alignment

git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
cmake -B build
cmake --build build --config Release

wget https://huggingface.co/KBLab/kb-whisper-tiny/resolve/main/ggml-model-q5_0.bin # Quantized version
# wget https://huggingface.co/KBLab/kb-whisper-tiny/resolve/main/ggml-model.bin # Non-quantized version

./build/bin/whisper-cli -m ggml-model-q5_0.bin ../audio.wav

from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import AutoProcessor

model_id = "KBLab/kb-whisper-tiny"
processor = AutoProcessor.from_pretrained(model_id, cache_dir="cache")
model = ORTModelForSpeechSeq2Seq.from_pretrained(
    model_id,
    cache_dir="cache",
    subfolder="onnx",
)

import soundfile as sf
audio = sf.read("audio.wav")

inputs = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors="pt")
gen_tokens = model.generate(**inputs, max_length=300)
processor.decode(gen_tokens[0], skip_special_tokens=True)

Vesterbacka, L., Rekathati, F., Kurtz, R., Sikora, J., Toftgård, A. (2025) Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition. Proc. Interspeech 2025, 758-762, doi: 10.21437/Interspeech.2025-2646

@inproceedings{vesterbacka25_interspeech,
  title     = {{Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition}},
  author    = {{Leonora Vesterbacka and Faton Rekathati and Robin Kurtz and Justyna Sikora and Agnes Toftgård}},
  year      = {{2025}},
  booktitle = {{Interspeech 2025}},
  pages     = {{758--762}},
  doi       = {{10.21437/Interspeech.2025-2646}},
  issn      = {{2958-1796}},
}