Create README.md

README.md

@@ -0,0 +1,67 @@
+---
+language:
+- ht
+tags:
+- audio
+- automatic-speech-recognition
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper small model for CTranslate2
+
+This repository contains the conversion of [YassineKader/whisper-small-haitian](https://huggingface.co/YassineKader/whisper-small-haitian) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
+
+This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
+
+## Example
+
+```python
+import ctranslate2
+import librosa
+import transformers
+from datetime import datetime
+# Load and resample the audio file.
+audio, _ = librosa.load("audio1.wav", sr=16000, mono=True)
+# Compute the features of the first 30 seconds of audio.
+processor = transformers.WhisperProcessor.from_pretrained("YassineKader/whisper-small-haitian")
+inputs = processor(audio, return_tensors="np", sampling_rate=16000)
+features = ctranslate2.StorageView.from_array(inputs.input_features)
+# Load the model on CPU.
+model = ctranslate2.models.Whisper("whisper-small-HT")
+# Detect the language.
+results = model.detect_language(features)
+language, probability = results[0][0]
+print("Detected language %s with probability %f" % (language, probability))
+print(datetime.now())
+# Describe the task in the prompt.
+# See the prompt format in https://github.com/openai/whisper.
+prompt = processor.tokenizer.convert_tokens_to_ids(
+    [
+        "<|startoftranscript|>",
+        language,
+        "<|transcribe|>",
+        "<|notimestamps|>",  # Remove this token to generate timestamps.
+    ]
+)
+# Run generation for the 30-second window.
+results = model.generate(features, [prompt])
+transcription = processor.decode(results[0].sequences_ids[0])
+
+print(datetime.now())
+print(transcription)
+```
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model guillaumekln/faster-whisper-small --output_dir faster-whisper-small-ht --copy_files tokenizer.json --quantization float32
+```
+
+Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
+
+## More information
+
+**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-small).**