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whisper-tiny-it-multi-ggml

Automatic Speech Recognition
Italian
whisper
whisper.cpp
ggml
italian
localai
quantized
Model card Files
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whisper-tiny-it-multi-ggml
File size: 2,632 Bytes
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---
language: it
license: mit
tags:
- whisper
- whisper.cpp
- ggml
- automatic-speech-recognition
- italian
- localai
- quantized
datasets:
- mozilla-foundation/common_voice_25_0
- facebook/multilingual_librispeech
- facebook/voxpopuli
- google/fleurs
base_model: openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
---

# whisper-tiny-it-multi-ggml

[whisper.cpp](https://github.com/ggerganov/whisper.cpp) GGML quantizations of [LocalAI-io/whisper-tiny-it-multi](https://huggingface.co/LocalAI-io/whisper-tiny-it-multi) for fast CPU/GPU inference.

**Author:** Ettore Di Giacinto

Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. These models can be used directly with [LocalAI](https://localai.io) and any whisper.cpp-based runtime.

## Files

| File | Quantization | Description |
|------|--------------|-------------|
| `ggml-model-f16.bin` | float16 | Full precision (no quantization) — highest quality |
| `ggml-model-q8_0.bin` | int8 | 8-bit quantization — minimal quality loss |
| `ggml-model-q5_0.bin` | int5 | 5-bit quantization — good quality/size tradeoff |
| `ggml-model-q4_0.bin` | int4 | 4-bit quantization — smallest size, fastest |

## Training

Fine-tuned [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) (39M params) on **Common Voice 25.0 + MLS + VoxPopuli + FLEURS Italian**.

See [LocalAI-io/whisper-tiny-it-multi](https://huggingface.co/LocalAI-io/whisper-tiny-it-multi) for the full safetensors model and detailed WER results.

## Usage

### whisper.cpp

```bash
# Download a quant
huggingface-cli download LocalAI-io/whisper-tiny-it-multi-ggml ggml-model-q5_0.bin --local-dir .

# Run
./whisper-cli -m ggml-model-q5_0.bin -f audio.wav -l it
```

### whisper.cpp Python bindings (pywhispercpp)

```python
from pywhispercpp.model import Model

model = Model("ggml-model-q5_0.bin", language="it")
segments = model.transcribe("audio.wav")
for seg in segments:
    print(seg.text)
```

### LocalAI

```yaml
# In your LocalAI model config
name: whisper-tiny-it-multi
backend: whisper
parameters:
  model: ggml-model-q5_0.bin
```

## Links

- **HF Safetensors:** [LocalAI-io/whisper-tiny-it-multi](https://huggingface.co/LocalAI-io/whisper-tiny-it-multi)
- **CTranslate2 INT8:** [LocalAI-io/whisper-tiny-it-multi-ct2-int8](https://huggingface.co/LocalAI-io/whisper-tiny-it-multi-ct2-int8)
- **Code:** [github.com/localai-org/whisper-it](https://github.com/localai-org/whisper-it)
- **whisper.cpp:** [github.com/ggerganov/whisper.cpp](https://github.com/ggerganov/whisper.cpp)
- **LocalAI:** [github.com/mudler/LocalAI](https://github.com/mudler/LocalAI)