Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,90 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language: it
|
| 3 |
+
license: mit
|
| 4 |
+
tags:
|
| 5 |
+
- whisper
|
| 6 |
+
- automatic-speech-recognition
|
| 7 |
+
- italian
|
| 8 |
+
- localai
|
| 9 |
+
datasets:
|
| 10 |
+
- mozilla-foundation/common_voice_25_0
|
| 11 |
+
- facebook/multilingual_librispeech
|
| 12 |
+
- facebook/voxpopuli
|
| 13 |
+
base_model: openai/whisper-medium
|
| 14 |
+
pipeline_tag: automatic-speech-recognition
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
# whisper-medium-it-multi
|
| 18 |
+
|
| 19 |
+
Fine-tuned [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) (769M params) for Italian ASR on multiple datasets.
|
| 20 |
+
|
| 21 |
+
**Author:** Ettore Di Giacinto
|
| 22 |
+
|
| 23 |
+
Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. This model can be used directly with [LocalAI](https://localai.io).
|
| 24 |
+
|
| 25 |
+
## Usage with LocalAI
|
| 26 |
+
|
| 27 |
+
This model is ready to use with [LocalAI](https://localai.io) via the `whisperx` backend.
|
| 28 |
+
|
| 29 |
+
Save the following as `whisperx-medium-it-multi.yaml` in your LocalAI models directory:
|
| 30 |
+
|
| 31 |
+
```yaml
|
| 32 |
+
name: whisperx-medium-it-multi
|
| 33 |
+
backend: whisperx
|
| 34 |
+
known_usecases:
|
| 35 |
+
- transcript
|
| 36 |
+
parameters:
|
| 37 |
+
model: LocalAI-io/whisper-medium-it-multi-ct2-int8
|
| 38 |
+
language: it
|
| 39 |
+
```
|
| 40 |
+
|
| 41 |
+
Then transcribe audio via the OpenAI-compatible endpoint:
|
| 42 |
+
|
| 43 |
+
```bash
|
| 44 |
+
curl http://localhost:8080/v1/audio/transcriptions \
|
| 45 |
+
-H "Content-Type: multipart/form-data" \
|
| 46 |
+
-F file="@audio.mp3" \
|
| 47 |
+
-F model="whisperx-medium-it-multi"
|
| 48 |
+
```
|
| 49 |
+
|
| 50 |
+
## Results
|
| 51 |
+
|
| 52 |
+
Evaluated on combined test set (Common Voice + MLS + VoxPopuli):
|
| 53 |
+
|
| 54 |
+
| Step | WER |
|
| 55 |
+
|------|-----|
|
| 56 |
+
| 1000 | 17.55% |
|
| 57 |
+
| 3000 | 16.71% |
|
| 58 |
+
| 5000 | 14.00% |
|
| 59 |
+
| 7000 | 13.02% |
|
| 60 |
+
| 9000 | 12.10% |
|
| 61 |
+
| 10000 | **12.37%** |
|
| 62 |
+
|
| 63 |
+
## Training Details
|
| 64 |
+
|
| 65 |
+
- **Base model:** openai/whisper-medium (769M parameters)
|
| 66 |
+
- **Datasets:** Common Voice 25.0 Italian (173k) + MLS Italian (60k) + VoxPopuli Italian (23k) = 255k train samples
|
| 67 |
+
- **Steps:** 10,000
|
| 68 |
+
- **Precision:** bf16 on NVIDIA GB10
|
| 69 |
+
|
| 70 |
+
## Usage
|
| 71 |
+
|
| 72 |
+
### Transformers
|
| 73 |
+
|
| 74 |
+
```python
|
| 75 |
+
from transformers import pipeline
|
| 76 |
+
|
| 77 |
+
pipe = pipeline("automatic-speech-recognition", model="LocalAI-io/whisper-medium-it-multi")
|
| 78 |
+
result = pipe("audio.mp3", generate_kwargs={"language": "it", "task": "transcribe"})
|
| 79 |
+
print(result["text"])
|
| 80 |
+
```
|
| 81 |
+
|
| 82 |
+
### CTranslate2 / faster-whisper
|
| 83 |
+
|
| 84 |
+
For optimized CPU inference: [LocalAI-io/whisper-medium-it-multi-ct2-int8](https://huggingface.co/LocalAI-io/whisper-medium-it-multi-ct2-int8)
|
| 85 |
+
|
| 86 |
+
## Links
|
| 87 |
+
|
| 88 |
+
- **CTranslate2 INT8:** [LocalAI-io/whisper-medium-it-multi-ct2-int8](https://huggingface.co/LocalAI-io/whisper-medium-it-multi-ct2-int8)
|
| 89 |
+
- **Project:** [github.com/localai-org/italian-whisper](https://github.com/localai-org/italian-whisper)
|
| 90 |
+
- **LocalAI:** [github.com/mudler/LocalAI](https://github.com/mudler/LocalAI)
|