Update README.md

README.md

@@ -1,90 +1,28 @@
----
-library_name: transformers
-license: bsd-3-clause
-base_model: MIT/ast-finetuned-speech-commands-v2
-tags:
-- generated_from_trainer
-datasets:
-- audiofolder
-metrics:
-- precision
-- recall
-- f1
-model-index:
-- name: ast-finetuned-keyword-spotting
-  results:
-  - task:
-      name: Audio Classification
-      type: audio-classification
-    dataset:
-      name: audiofolder
-      type: audiofolder
-      config: default
-      split: validation
-      args: default
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.9861935383961439
-    - name: Recall
-      type: recall
-      value: 0.9861649413727126
-    - name: F1
-      type: f1
-      value: 0.9861100898918743
----
-
-<!-- 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. -->
-
-# ast-finetuned-keyword-spotting
-
-This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on the audiofolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0685
-- Precision: 0.9862
-- Recall: 0.9862
-- F1: 0.9861
-
-## 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: 5e-05
-- train_batch_size: 64
-- eval_batch_size: 64
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
-| 0.0682        | 1.0   | 1630 | 0.0976          | 0.9752    | 0.9751 | 0.9749 |
-| 0.0179        | 2.0   | 3260 | 0.0743          | 0.9847    | 0.9846 | 0.9846 |
-| 0.0008        | 3.0   | 4890 | 0.0685          | 0.9862    | 0.9862 | 0.9861 |
-
-
-### Framework versions
-
-- Transformers 4.51.3
-- Pytorch 2.7.0+cu128
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+# Audio Spectrogram Transformer (AST) Fine-Tuned on MLCommons Multilingual Spoken Words + Google Speech Commands
+
+## Model Details
+- **Model name:** `my-ast-mlcommons-speech-commands`
+- **Architecture:** Audio Spectrogram Transformer (AST)
+- **Base pre-trained checkpoint:** MIT AST fine-tuned on Google Speech Commands v0.02
+- **Fine-tuning dataset:** Custom dataset drawn from MLCommons Multilingual Spoken Words corpus, augmented with `_silence_` and `_unknown_` categories sampled from Google Speech Commands v0.02
+- **License:** Apache 2.0
+- **Framework:** PyTorch
+
+## Use Case
+- **Primary use case:** Keyword spotting and spoken-word classification in multilingual voice interfaces
+- **Territory:** Real-time small-vocabulary speech recognition for embedded and mobile devices
+- **Out of scope:** Large-vocabulary continuous speech recognition, speaker identification, emotion recognition
+
+## Model Inputs and Outputs
+- **Input:** 16 kHz mono audio, 1-second clips (or padded/truncated to 1 sec), converted to log-mel spectrograms with 128 mel bins and 10 ms hop length
+- **Output:** Softmax over 80 classes (indices 0–79). Classes mapping:
+  ```json
+  {
+    "0": "_silence_",
+    "1": "_unknown_",
+    "2": "air",
+    // ... 3–9 omitted for brevity ...
+    "9": "cake",
+    "10": "car",
+    // ... up to 79: "zoo"
+  }