README.md

---
language:
- pl
tags:
- audio
- automatic-speech-recognition
- transformers.js
pipeline_tag: automatic-speech-recognition
license: mit
library_name: transformers
---

# Polish Distil-Whisper: distil-large-v3

Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430).

It is a distilled version of the Whisper model that is **3 times faster**, 49% smaller. This is the repository for distil-large-v3-pl, a distilled variant of [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3).


## Usage

Distil-Whisper is supported in Hugging Face 🤗 Transformers from version 4.35 onwards. To run the model, first 
install the latest version of the Transformers library. For this example, we'll also install 🤗 Datasets to load toy 
audio dataset from the Hugging Face Hub:

```bash
pip install --upgrade pip
pip install --upgrade transformers accelerate datasets[audio]
```

### Short-Form Transcription

The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe short-form audio files (< 30-seconds) as follows:

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


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "Aspik101/distil-whisper-large-v3-pl"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    torch_dtype=torch_dtype,
    device=device,
)

dataset = load_dataset("mozilla-foundation/common_voice_13_0", "pl", split="test")
sample = dataset[0]["audio"]

result = pipe(sample)
print(result["text"])
```

To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
```diff
- result = pipe(sample)
+ result = pipe("audio.mp3")
```

### Long-Form Transcription

Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm 
is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).

To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For Distil-Whisper, a chunk length of 15-seconds
is optimal. To activate batching, pass the argument `batch_size`:

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


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "Aspik101/distil-whisper-large-v3-pl"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=15,
    batch_size=16,
    torch_dtype=torch_dtype,
    device=device,
)

dataset = load_dataset("mozilla-foundation/common_voice_13_0", "pl", split="test")
sample = dataset[0]["audio"]

result = pipe(sample)
print(result["text"])
```

<!---
**Tip:** The pipeline can also be used to transcribe an audio file from a remote URL, for example:

```python
result = pipe("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav")
```
--->