Update README.md

README.md

@@ -1,3 +1,75 @@
----
-license: mit
----
+---
+license: mit
+language: et
+tags:
+- audio
+- automatic-speech-recognition
+#widget:
+#- example_title: Librispeech sample 1
+#  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+#- example_title: Librispeech sample 2
+#  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+pipeline_tag: automatic-speech-recognition
+base_model:
+- openai/whisper-large-v3
+library_name: transformers
+---
+
+## Introduction
+
+This model is OpenAI Whisper large-v3, finetuned on ~770 hours of manually created subtitles from Estonian TV (ETV). 
+Therefore, this model does not always create verbatim (word-by-word) subtitles but often rephrases the sentences and
+compresses text, especially in the case of spontaneous speech, hestitations, repetitions, etc. However, the length
+of the generated text chunks almost always conforms to the ETV subtitle requirements (48 characters per line).
+
+## Usage
+
+
+
+It's a finetuned vesion of Whisper large-v3-turbo and can be therefore used via Hugging Face 🤗 Transformers. To run the model, first install the Transformers 
+library. For this example, we'll also install 🤗 Accelerate to reduce the model loading time:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade transformers accelerate
+```
+
+The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
+class to transcribe audios of arbitrary length:
+
+```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 = "TalTechNLP/whisper-large-v3-et-subs"
+
+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,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+audio = "sample.mp3" 
+
+result = pipe(sample, generate_kwargs={"task": "transcribe", "language": "et"})
+print(result)
+```
+
+## Evaluation results
+
+TODO