Upload README.md

README.md

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+---
+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")
+```
+--->