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.ipynb_checkpoints/README-checkpoint.md

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+---
+language: 
+- en
+- zh
+- de
+- es
+- ru
+- ko
+- fr
+- ja
+- pt
+- tr
+- pl
+- ca
+- nl
+- ar
+- sv
+- it
+- id
+- hi
+- fi
+- vi
+- he
+- uk
+- el
+- ms
+- cs
+- ro
+- da
+- hu
+- ta
+- no
+- th
+- ur
+- hr
+- bg
+- lt
+- la
+- mi
+- ml
+- cy
+- sk
+- te
+- fa
+- lv
+- bn
+- sr
+- az
+- sl
+- kn
+- et
+- mk
+- br
+- eu
+- is
+- hy
+- ne
+- mn
+- bs
+- kk
+- sq
+- sw
+- gl
+- mr
+- pa
+- si
+- km
+- sn
+- yo
+- so
+- af
+- oc
+- ka
+- be
+- tg
+- sd
+- gu
+- am
+- yi
+- lo
+- uz
+- fo
+- ht
+- ps
+- tk
+- nn
+- mt
+- sa
+- lb
+- my
+- bo
+- tl
+- mg
+- as
+- tt
+- haw
+- ln
+- ha
+- ba
+- jw
+- su
+tags:
+  - audio
+  - automatic-speech-recognition
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper large-v3-turbo model for CTranslate2
+
+This repository contains the conversion of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) 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/systran/faster-whisper).
+
+## Example with batch inference
+
+```python
+import time
+
+from faster_whisper import WhisperModel, BatchedInferencePipeline
+from faster_whisper.audio import decode_audio
+
+model = WhisperModel("Infomaniak-AI/faster-whisper-large-v3-turbo",
+                     device="cuda",
+                     num_workers=4,
+                     compute_type='float16')
+
+batch = BatchedInferencePipeline(model=model,
+                                 use_vad_model=True,
+                                 chunk_length=30)
+
+audio = decode_audio("audio.mp3", sampling_rate=model.feature_extractor.sampling_rate)
+start_time = time.time()
+segment_generator, info = batch.transcribe(audio,
+                                           batch_size=32,
+                                           beam_size=5,
+                                           task="transcribe",
+                                           word_timestamps=True,
+                                           suppress_blank=True)
+segments = []
+text = ""
+for segment in segment_generator:
+    segments.append(segment)
+    text = text + segment.text
+
+print("--- %s seconds ---" % (time.time() - start_time))
+
+```
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model openai/whisper-large-v3-turbo --output_dir whisper-large-v3-turbo --copy_files tokenizer.json preprocessor_config.json --quantization float16
+```
+
+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-large-v3-turbo).**

README.md

@@ -1,3 +1,162 @@
----
-license: apache-2.0
----
+---
+language: 
+- en
+- zh
+- de
+- es
+- ru
+- ko
+- fr
+- ja
+- pt
+- tr
+- pl
+- ca
+- nl
+- ar
+- sv
+- it
+- id
+- hi
+- fi
+- vi
+- he
+- uk
+- el
+- ms
+- cs
+- ro
+- da
+- hu
+- ta
+- no
+- th
+- ur
+- hr
+- bg
+- lt
+- la
+- mi
+- ml
+- cy
+- sk
+- te
+- fa
+- lv
+- bn
+- sr
+- az
+- sl
+- kn
+- et
+- mk
+- br
+- eu
+- is
+- hy
+- ne
+- mn
+- bs
+- kk
+- sq
+- sw
+- gl
+- mr
+- pa
+- si
+- km
+- sn
+- yo
+- so
+- af
+- oc
+- ka
+- be
+- tg
+- sd
+- gu
+- am
+- yi
+- lo
+- uz
+- fo
+- ht
+- ps
+- tk
+- nn
+- mt
+- sa
+- lb
+- my
+- bo
+- tl
+- mg
+- as
+- tt
+- haw
+- ln
+- ha
+- ba
+- jw
+- su
+tags:
+  - audio
+  - automatic-speech-recognition
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper large-v3-turbo model for CTranslate2
+
+This repository contains the conversion of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) 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/systran/faster-whisper).
+
+## Example with batch inference
+
+```python
+import time
+
+from faster_whisper import WhisperModel, BatchedInferencePipeline
+from faster_whisper.audio import decode_audio
+
+model = WhisperModel("Infomaniak-AI/faster-whisper-large-v3-turbo",
+                     device="cuda",
+                     num_workers=4,
+                     compute_type='float16')
+
+batch = BatchedInferencePipeline(model=model,
+                                 use_vad_model=True,
+                                 chunk_length=30)
+
+audio = decode_audio("audio.mp3", sampling_rate=model.feature_extractor.sampling_rate)
+start_time = time.time()
+segment_generator, info = batch.transcribe(audio,
+                                           batch_size=32,
+                                           beam_size=5,
+                                           task="transcribe",
+                                           word_timestamps=True,
+                                           suppress_blank=True)
+segments = []
+text = ""
+for segment in segment_generator:
+    segments.append(segment)
+    text = text + segment.text
+
+print("--- %s seconds ---" % (time.time() - start_time))
+
+```
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model openai/whisper-large-v3-turbo --output_dir whisper-large-v3-turbo --copy_files tokenizer.json preprocessor_config.json --quantization float16
+```
+
+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-large-v3-turbo).**

config.json

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+}

model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e76620f83d5f5b69efd3d87e3dc180c1bd21df9fbebacfd4335e5e1efcc018da
+size 1617884929

preprocessor_config.json

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+{
+  "chunk_length": 30,
+  "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
+}