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README.md

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+---
+language:
+- ml
+tags:
+- audio
+- automatic-speech-recognition
+license: mit
+datasets:
+- google/fleurs
+- thennal/IMaSC
+- mozilla-foundation/common_voice_11_0
+library_name: ctranslate2
+---
+
+# vegam-whipser-medium-ml 
+
+This is a conversion of [thennal/whisper-medium-ml](https://huggingface.co/thennal/whisper-medium-ml) 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).
+
+## Installation
+
+- Install [faster-whisper](https://github.com/guillaumekln/faster-whisper). More details about installation can be [found here in faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master#installation).
+
+```
+pip install faster-whisper
+```
+
+- Install  [git-lfs](https://git-lfs.com/) for using this project. Note that git-lfs is just for downloading model from hugging-face.
+
+```
+apt-get install git-lfs
+```
+
+- Download the model weights
+
+```
+git lfs install
+git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml
+```
+
+## Usage
+
+```
+from faster_whisper import WhisperModel
+
+model_path = "vegam-whisper-medium-ml"
+
+# Run on GPU with FP16
+model = WhisperModel(model_path, device="cuda", compute_type="float16")
+
+# or run on GPU with INT8
+# model = WhisperModel(model_path, device="cuda", compute_type="int8_float16")
+# or run on CPU with INT8
+# model = WhisperModel(model_path, device="cpu", compute_type="int8")
+
+segments, info = model.transcribe("audio.mp3", beam_size=5)
+
+print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
+
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+```
+
+## Example
+
+```
+from faster_whisper import WhisperModel
+
+model_path = "vegam-whisper-medium-ml"
+
+model = WhisperModel(model_path, device="cuda", compute_type="float16")
+
+
+segments, info = model.transcribe("00b38e80-80b8-4f70-babf-566e848879fc.webm", beam_size=5)
+
+print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
+
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+```
+
+> Detected language 'ta' with probability 0.353516
+
+> [0.00s -> 4.74s] പാലം കടുക്കുവോളം നാരായണ പാലം കടന്നാലൊ കൂരായണ
+
+Note: The audio file [00b38e80-80b8-4f70-babf-566e848879fc.webm](https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml/blob/main/00b38e80-80b8-4f70-babf-566e848879fc.webm) is from [Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and is stored along with model weights.
+## Conversion Details
+
+This conversion was possible with wonderful [CTranslate2 library](https://github.com/OpenNMT/CTranslate2) leveraging the [Transformers converter for OpenAI Whisper](https://opennmt.net/CTranslate2/guides/transformers.html#whisper).The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml
+```
+
+## Many Thanks to
+
+- Creators of CTranslate2 and faster-whisper
+- Thennal D K
+- Santhosh Thottingal
+ 

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+}

model.bin

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