Training in progress, step 15500

README.md

@@ -1,71 +1,207 @@
 ---
-license: mit
-language:
-- en
-datasets:
-- speechbrain/LoquaciousSet
-base_model:
-- openai/whisper-large-v3-turbo
-- HuggingFaceTB/SmolLM3-3B
-pipeline_tag: automatic-speech-recognition
+base_model: Qwen/Qwen3-1.7B
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- asr
-- speech-recognition
-- audio
-- smollm
-- whisper
-- mlp
+- base_model:adapter:Qwen/Qwen3-1.7B
+- lora
+- transformers
 ---
 
-# Tiny Audio
+# Model Card for Model ID
 
-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.
+<!-- Provide a quick summary of what the model is/does. -->
 
-## 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
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
 
 ## Training Details
 
-| | |
-|---|---|
-| **Dataset** | LoquaciousSet (25,000 hours) |
-| **Hardware** | Single NVIDIA A40 40GB |
-| **Training Time** | ~24 hours |
-| **Cost** | ~$12 |
-| **Trainable Parameters** | ~12M (projector only) |
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
 
-## Performance
+[More Information Needed]
 
-**Word Error Rate (WER): 12.14%** on LoquaciousSet test set.
+## Glossary [optional]
 
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-## Usage
+[More Information Needed]
 
-```python
-from transformers import pipeline
+## More Information [optional]
 
-pipe = pipeline("automatic-speech-recognition", model="mazesmazes/tiny-audio", trust_remote_code=True)
+[More Information Needed]
 
-result = pipe("path/to/audio.wav")
-print(result["text"])
-```
+## Model Card Authors [optional]
 
-## Limitations
+[More Information Needed]
 
-- 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
+## Model Card Contact
 
-## Learn More
+[More Information Needed]
+### Framework versions
 
-- **[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
+- PEFT 0.18.0

adapter_model.safetensors

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generation_config.json

@@ -6,9 +6,9 @@
   ],
   "length_penalty": 1.0,
   "max_new_tokens": 256,
-  "no_repeat_ngram_size": 3,
+  "no_repeat_ngram_size": 0,
   "num_beams": 1,
   "pad_token_id": 151643,
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+  "repetition_penalty": 1.0,
   "transformers_version": "5.0.0.dev0"
 }

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