whisper-large-v3-turbo-ct2

whisper-large-v3-turbo-ct2/.gitattributes

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whisper-large-v3-turbo-ct2/README.md

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+---
+license: apache-2.0
+datasets:
+- ivrit-ai/crowd-transcribe-v5
+language:
+- he
+base_model:
+- openai/whisper-large-v3-turbo
+---
+
+This is ivrit.ai's faster-whisper model, based on the ivrit-ai/whisper-large-v3-turbo Whisper model.
+
+Training data includes 295 hours of volunteer-transcribed speech from the ivrit-ai/crowd-transcribe-v5 dataset, as well as 93 hours of professional transcribed speech from other sources.
+
+Release date: TBD
+
+# Prerequisites
+
+pip3 install faster_whisper
+
+# Usage
+
+```
+import faster_whisper
+model = faster_whisper.WhisperModel('ivrit-ai/whisper-large-v3-turbo-ct2')
+
+segs, _ = model.transcribe('media-file', language='he')
+
+texts = [s.text for s in segs]
+
+transcribed_text = ' '.join(texts)
+print(f'Transcribed text: {transcribed_text}')
+```

whisper-large-v3-turbo-ct2/config.json

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whisper-large-v3-turbo-ct2/model.bin

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whisper-large-v3-turbo-ct2/preprocessor_config.json

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+{
+  "chunk_length": 30,
+  "dither": 0.0,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}