Model save

.gitattributes

@@ -1,3 +1,4 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
 tokenizer_config.json -filter -diff -merge text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,71 +1,123 @@
 ---
-license: mit
-language:
-- en
-datasets:
-- speechbrain/LoquaciousSet
-base_model:
-- openai/whisper-large-v3-turbo
-- HuggingFaceTB/SmolLM3-3B
-pipeline_tag: automatic-speech-recognition
+library_name: transformers
 tags:
-- asr
-- speech-recognition
-- audio
-- smollm
-- whisper
-- mlp
+- generated_from_trainer
+model-index:
+- name: tiny-audio
+  results: []
 ---
 
-# Tiny Audio
-
-A speech recognition model trained in 24 hours on a single GPU for ~$12. Built with the [Tiny Audio](https://github.com/alexkroman/tiny-audio) codebase—a minimal, hackable framework for training ASR models.
-
-## Architecture
-
-```
-Audio (16kHz) → Whisper Encoder (frozen) → MLP Projector (trained) → SmolLM3-3B (frozen) → Text
-```
-
-**MLP Projector:**
-- Convolutional downsampling: 4x sequence compression via two stride-2 conv layers
-- Linear (1280 → 2048) → GELU → Linear (2048 → 2048)
-- Output normalization: RMSNorm
-
-## Training Details
-
-| | |
-|---|---|
-| **Dataset** | LoquaciousSet (25,000 hours) |
-| **Hardware** | Single NVIDIA A40 40GB |
-| **Training Time** | ~24 hours |
-| **Cost** | ~$12 |
-| **Trainable Parameters** | ~12M (projector only) |
-
-## Performance
-
-**Word Error Rate (WER): 12.14%** on LoquaciousSet test set.
-
-
-## Usage
-
-```python
-from transformers import pipeline
-
-pipe = pipeline("automatic-speech-recognition", model="mazesmazes/tiny-audio", trust_remote_code=True)
-
-result = pipe("path/to/audio.wav")
-print(result["text"])
-```
-
-## Limitations
-
-- English only
-- Optimized for 16kHz audio; other sample rates are resampled automatically
-- Performance may degrade on heavily accented speech, noisy environments, or domain-specific jargon
-- Maximum audio length limited by context window
-
-## Learn More
-
-- **[Train your own model](https://github.com/alexkroman/tiny-audio)** — The full codebase with training scripts
-- **[Free 3.5-hour course](https://github.com/alexkroman/tiny-audio/blob/main/docs/course/0-course-overview.md)** — Build your own ASR system from scratch
+<!-- 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. -->
+
+# tiny-audio
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2566
+
+## 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: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 936
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss |
+|:-------------:|:------:|:-----:|:---------------:|
+| 0.2888        | 0.0149 | 1000  | 0.2819          |
+| 0.3565        | 0.0298 | 2000  | 0.2919          |
+| 0.3189        | 0.0447 | 3000  | 0.2879          |
+| 0.3274        | 0.0596 | 4000  | 0.2929          |
+| 0.3231        | 0.0745 | 5000  | 0.2870          |
+| 0.3270        | 0.0894 | 6000  | 0.2853          |
+| 0.3486        | 0.1043 | 7000  | 0.2860          |
+| 0.3066        | 0.1192 | 8000  | 0.2865          |
+| 0.3487        | 0.1341 | 9000  | 0.2866          |
+| 0.3307        | 0.1490 | 10000 | 0.2871          |
+| 0.3419        | 0.1639 | 11000 | 0.2852          |
+| 0.3601        | 0.1788 | 12000 | 0.2848          |
+| 0.3156        | 0.1936 | 13000 | 0.2860          |
+| 0.3098        | 0.2085 | 14000 | 0.2830          |
+| 0.3133        | 0.2234 | 15000 | 0.2851          |
+| 0.3269        | 0.2383 | 16000 | 0.2826          |
+| 0.3257        | 0.2532 | 17000 | 0.2822          |
+| 0.3281        | 0.2681 | 18000 | 0.2822          |
+| 0.3941        | 0.2830 | 19000 | 0.2813          |
+| 0.3875        | 0.2979 | 20000 | 0.2854          |
+| 0.3214        | 0.3128 | 21000 | 0.2795          |
+| 0.2914        | 0.3277 | 22000 | 0.2792          |
+| 0.2951        | 0.3426 | 23000 | 0.2805          |
+| 0.3343        | 0.3575 | 24000 | 0.2779          |
+| 0.3252        | 0.3724 | 25000 | 0.2771          |
+| 0.3027        | 0.3873 | 26000 | 0.2768          |
+| 0.3287        | 0.4022 | 27000 | 0.2759          |
+| 0.3208        | 0.4171 | 28000 | 0.2749          |
+| 0.3402        | 0.4320 | 29000 | 0.2730          |
+| 0.2928        | 0.4469 | 30000 | 0.2726          |
+| 0.3085        | 0.4618 | 31000 | 0.2737          |
+| 0.3073        | 0.4767 | 32000 | 0.2705          |
+| 0.3471        | 0.4916 | 33000 | 0.2708          |
+| 0.2945        | 0.5065 | 34000 | 0.2690          |
+| 0.3294        | 0.5214 | 35000 | 0.2696          |
+| 0.3095        | 0.5363 | 36000 | 0.2679          |
+| 0.3152        | 0.5512 | 37000 | 0.2659          |
+| 0.3035        | 0.5660 | 38000 | 0.2674          |
+| 0.3342        | 0.5809 | 39000 | 0.2656          |
+| 0.3242        | 0.5958 | 40000 | 0.2653          |
+| 0.2789        | 0.6107 | 41000 | 0.2643          |
+| 0.3082        | 0.6256 | 42000 | 0.2643          |
+| 0.3174        | 0.6405 | 43000 | 0.2633          |
+| 0.2730        | 0.6554 | 44000 | 0.2628          |
+| 0.2934        | 0.6703 | 45000 | 0.2609          |
+| 0.2944        | 0.6852 | 46000 | 0.2606          |
+| 0.3111        | 0.7001 | 47000 | 0.2614          |
+| 0.3431        | 0.7150 | 48000 | 0.2605          |
+| 0.3226        | 0.7299 | 49000 | 0.2601          |
+| 0.2735        | 0.7448 | 50000 | 0.2591          |
+| 0.3208        | 0.7597 | 51000 | 0.2590          |
+| 0.3208        | 0.7746 | 52000 | 0.2584          |
+| 0.3021        | 0.7895 | 53000 | 0.2578          |
+| 0.2730        | 0.8044 | 54000 | 0.2583          |
+| 0.2938        | 0.8193 | 55000 | 0.2581          |
+| 0.2894        | 0.8342 | 56000 | 0.2574          |
+| 0.2781        | 0.8491 | 57000 | 0.2572          |
+| 0.3003        | 0.8640 | 58000 | 0.2568          |
+| 0.2719        | 0.8789 | 59000 | 0.2568          |
+| 0.2878        | 0.8938 | 60000 | 0.2567          |
+| 0.3058        | 0.9087 | 61000 | 0.2568          |
+| 0.3036        | 0.9236 | 62000 | 0.2568          |
+| 0.3050        | 0.9384 | 63000 | 0.2568          |
+| 0.3244        | 0.9533 | 64000 | 0.2567          |
+| 0.3187        | 0.9682 | 65000 | 0.2566          |
+| 0.3016        | 0.9831 | 66000 | 0.2566          |
+| 0.2697        | 0.9980 | 67000 | 0.2566          |
+
+
+### Framework versions
+
+- Transformers 5.0.0.dev0
+- Pytorch 2.8.0+cu128
+- Datasets 3.6.0
+- Tokenizers 0.22.1

asr_pipeline.py

@@ -215,7 +215,8 @@ class SpeakerDiarizer:
         # Prepare audio input
         if isinstance(audio, np.ndarray):
             # pyannote expects {"waveform": tensor, "sample_rate": int}
-            waveform = torch.from_numpy(audio).unsqueeze(0)  # Add channel dim
+            # Copy array to ensure it's writable (avoids PyTorch warning)
+            waveform = torch.from_numpy(audio.copy()).unsqueeze(0)  # Add channel dim
             if waveform.dim() == 1:
                 waveform = waveform.unsqueeze(0)
             audio_input = {"waveform": waveform, "sample_rate": sample_rate}