Quantizated models added

fp16/README.md

@@ -0,0 +1,54 @@
+---
+language:
+  - hu
+tags:
+  - audio
+  - automatic-speech-recognition
+datasets:
+- mozilla-foundation/common_voice_16_0
+base_model: openai/whisper-base
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper base model for CTranslate2
+
+This repository contains the conversion of a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. Fine-tune is made by [@sarpba](https://huggingface.co/sarpba) on the Common Voice 16 dataset of Mozilla Foundation.
+
+This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
+
+## Example
+
+```python
+from faster_whisper import WhisperModel
+
+model = WhisperModel("base")
+
+segments, info = model.transcribe("audio.mp3")
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+```
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model Hungarians/whisper-base-cv16-hu-v2 --output_dir faster-whisper-base-cv16-v2-fp16.hu \
+    --quantization fp16 --low_cpu_mem_usage --copy_files tokenizer_config.json preprocessor_config.json
+```
+
+## HASH calculation
+
+Hash calculation is executed with md5hash in the directory of the model with:
+
+```
+find ./ -maxdepth 1 -type f -not -path '*/\.*'  -exec md5sum {} \; | tr -d ' ' | jq -R 'split("./") | {(.[1]): (.[0])}' | jq -s 'add' > hash.json
+
+```
+
+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/Hungarians/whisper-base-cv16-hu-v2).**

fp16/config.json

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fp16/hash.json

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fp16/model.bin

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fp16/preprocessor_config.json

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+{
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+  "feature_size": 80,
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+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

fp32/README.md

@@ -0,0 +1,88 @@
+---
+datasets:
+  - mozilla-foundation/common_voice_16_0
+language:
+- hu
+widget:
+  - example_title: Sample 1
+    src: >-
+      https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac
+  - example_title: Sample 2
+    src: >-
+      https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac
+license: apache-2.0
+base_model: openai/whisper-base
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: Whisper Base Hu v2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Whisper Base Hu v2
+
+This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 16.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1599
+- Wer Ortho: 12.6641
+- Wer: 11.4171 
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2.75e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant_with_warmup
+- lr_scheduler_warmup_steps: 500
+- training_steps: 15000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer Ortho | Wer     |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------:|
+| 0.199         | 0.33  | 1000  | 0.3838          | 36.7548   | 33.5517 |
+| 0.3037        | 0.67  | 2000  | 0.3131          | 31.2748   | 28.3664 |
+| 0.221         | 1.0   | 3000  | 0.2546          | 27.1739   | 24.1773 |
+| 0.1562        | 1.34  | 4000  | 0.2319          | 23.9341   | 21.3341 |
+| 0.1623        | 1.67  | 5000  | 0.2101          | 21.4079   | 18.9623 |
+| 0.077         | 2.01  | 6000  | 0.1818          | 18.5415   | 16.2852 |
+| 0.078         | 2.34  | 7000  | 0.1846          | 17.8339   | 15.7456 |
+| 0.0818        | 2.68  | 8000  | 0.1712          | 16.4669   | 14.5983 |
+| 0.0352        | 3.01  | 9000  | 0.1669          | 15.6178   | 14.0676 |
+| 0.0413        | 3.35  | 10000 | 0.1673          | 14.9464   | 13.4539 |
+| 0.0454        | 3.68  | 11000 | 0.1649          | 14.5459   | 12.7542 |
+| 0.0225        | 4.02  | 12000 | 0.1589          | 13.5885   | 12.2087 |
+| 0.0269        | 4.35  | 13000 | 0.1638          | 14.3864   | 12.8343 |
+| 0.0299        | 4.69  | 14000 | 0.1621          | 13.0555   | 11.7610 |
+| 0.0171        | 5.02  | 15000 | 0.1599          | 12.6641   | 11.4171 |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

fp32/config.json

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+{
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fp32/preprocessor_config.json

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+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

int8/README.md

@@ -0,0 +1,53 @@
+---
+language:
+  - hu
+tags:
+  - audio
+  - automatic-speech-recognition
+datasets:
+- mozilla-foundation/common_voice_16_0
+base_model: openai/whisper-base
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper base model for CTranslate2
+
+This repository contains the conversion of a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. Fine-tune is made by [@sarpba](https://huggingface.co/sarpba) on the Common Voice 16 dataset of Mozilla Foundation.
+
+This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
+
+## Example
+
+```python
+from faster_whisper import WhisperModel
+
+model = WhisperModel("base")
+
+segments, info = model.transcribe("audio.mp3")
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+```
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model Hungarians/whisper-base-cv16-hu-v2 --output_dir faster-whisper-base-cv16-v2-int8.hu \
+    --quantization int8 --low_cpu_mem_usage --copy_files tokenizer_config.json preprocessor_config.json
+```
+
+Note that the model weights are saved in INT8. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
+
+## HASH calculation
+
+Hash calculation is executed with md5hash in the directory of the model with:
+
+```
+find ./ -maxdepth 1 -type f -not -path '*/\.*'  -exec md5sum {} \; | tr -d ' ' | jq -R 'split("./") | {(.[1]): (.[0])}' | jq -s 'add' > hash.json
+```
+
+## More information
+
+**For more information about the original model, see its [model card](https://huggingface.co/Hungarians/whisper-base-cv16-hu-v2).**

int8/config.json

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+}

int8/hash.json

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+}

int8/model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5056fe79745315cbd0ebaec94bbc4dd4520b75c82ef87c9d7edd83ee23bfa8dd
+size 79120439

int8/preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 80,
+  "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
+}