初始化项目，由ModelHub XC社区提供模型

Model: thennal/whisper-medium-ml
Source: Original Platform

README.md

@@ -0,0 +1,98 @@
+---
+language:
+- ml
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
+- google/fleurs
+- thennal/IMaSC
+- thennal/ulca_ml
+- thennal/msc
+- thennal/indic_tts_ml
+metrics:
+- wer
+base_model: openai/whisper-medium
+model-index:
+- name: Whisper Medium Malayalam - Thennal D K
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: ml
+      split: test
+      args: ml
+    metrics:
+    - type: wer
+      value: 11.49
+      name: WER
+---
+
+<!-- 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 Medium Malayalam
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
+It achieves the following results on the evaluation set:
+- WER: 38.6207
+- CER: 7.3256
+
+Note that Whisper's normalization has major issues for languages like Malayalam, so the above scores are evaluated without using normalization. 
+With normalization (for a fair comparison with other models on this platform), the results are instead:
+- WER: 11.49
+
+[This Colab](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb) can be used as a starting point to further finetune the model.
+## Usage instructions
+Given an audio sample `audio` (this can be anything from a numpy array to a filepath), the following code generates transcriptions:
+```python
+from transformers import pipeline, WhisperProcessor
+
+processor = WhisperProcessor.from_pretrained("thennal/whisper-medium-ml")
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="ml", task="transcribe")
+asr = pipeline(
+        "automatic-speech-recognition", model="thennal/whisper-medium-ml", device=0,
+    )
+transcription = asr(audio, chunk_length_s=30, max_new_tokens=448, return_timestamps=False,  generate_kwargs={
+        "forced_decoder_ids": forced_decoder_ids, 
+        "do_sample": True,
+    })
+```
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 8000
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.26.0.dev0
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.1.dev0
+- Tokenizers 0.13.2