初始化项目，由ModelHub XC社区提供模型

Model: Bidwill/whisper-small-sanskrit Source: Original Platform
2026-05-14 10:32:40 +08:00
commit 5629cf3b96
14 changed files with 117185 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,96 @@
+---
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- hf-asr-leaderboard
+- generated_from_trainer
+datasets:
+- load_data
+metrics:
+- wer
+model-index:
+- name: Whisper Small Sanskrit_4 - Bidit Sadhukhan
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: load_data
+      type: load_data
+      config: validation
+      split: validation
+      args: validation
+    metrics:
+    - name: Wer
+      type: wer
+      value: 27.941506051098163
+---
+
+<!-- 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 Small Sanskrit_4 - Bidit Sadhukhan
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the load_data dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1193
+- Wer: 27.9415
+
+## 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: 2.5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 44
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 10000
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer     |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|
+| 0.1168        | 0.12  | 500   | 0.2069          | 47.5067 |
+| 0.0854        | 0.23  | 1000  | 0.1774          | 43.4951 |
+| 0.0766        | 0.35  | 1500  | 0.1659          | 40.7385 |
+| 0.0631        | 0.47  | 2000  | 0.1644          | 39.0968 |
+| 0.0629        | 0.58  | 2500  | 0.1379          | 34.7266 |
+| 0.0763        | 0.7   | 3000  | 0.1401          | 35.1916 |
+| 0.0515        | 0.82  | 3500  | 0.1343          | 34.8386 |
+| 0.0457        | 0.93  | 4000  | 0.1185          | 31.6114 |
+| 0.0302        | 1.05  | 4500  | 0.1315          | 33.1074 |
+| 0.0276        | 1.17  | 5000  | 0.1245          | 31.1127 |
+| 0.0234        | 1.28  | 5500  | 0.1265          | 30.9166 |
+| 0.0266        | 1.4   | 6000  | 0.1289          | 30.6029 |
+| 0.0186        | 1.52  | 6500  | 0.1230          | 30.1658 |
+| 0.0284        | 1.63  | 7000  | 0.1157          | 29.2414 |
+| 0.0182        | 1.75  | 7500  | 0.1125          | 27.6165 |
+| 0.024         | 1.87  | 8000  | 0.1143          | 28.5970 |
+| 0.0214        | 1.98  | 8500  | 0.1097          | 27.2972 |
+| 0.0055        | 2.1   | 9000  | 0.1152          | 28.2497 |
+| 0.0069        | 2.21  | 9500  | 0.1210          | 27.3364 |
+| 0.0101        | 2.33  | 10000 | 0.1193          | 27.9415 |
+
+
+### Framework versions
+
+- Transformers 4.35.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.14.0