Update README.md

README.md

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 ---
+library_name: transformers
 license: apache-2.0
+base_model: mistralai/Voxtral-Mini-3B-2507
+language:
+  - et
+  - en
+pipeline_tag: automatic-speech-recognition
+tags:
+  - audio
+  - speech
+  - voxtral
+  - estonian
+  - transcription
+  - translation
+  - audio-understanding
+  - full-finetune
 ---
+
+# Voxtral Mini 3B 2507 Estonian
+
+This model is a full-parameter fine-tune of
+[`mistralai/Voxtral-Mini-3B-2507`](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)
+for Estonian speech recognition and audio understanding. It was trained on a
+mixture of transcription, subtitle and stenogram generation, news writing,
+speech translation, summarization, and question-answering tasks.
+
+
+## Model details
+
+| Property | Value |
+|---|---|
+| Base model | `mistralai/Voxtral-Mini-3B-2507` |
+| Architecture | `VoxtralForConditionalGeneration` |
+| Primary language | Estonian (`et/est`) |
+| Additional output language | English (`en`) |
+| Fine-tuning method | Full-parameter supervised fine-tuning |
+| Selected checkpoint | 4,500 steps |
+| Weight precision | bfloat16 |
+| License | Apache 2.0, following the base model |
+
+## Intended uses
+
+The model is intended for research and development involving Estonian speech,
+including:
+
+- verbatim automatic speech recognition;
+- readable subtitle and parliamentary stenogram generation;
+- summaries and news stories based on broadcast audio;
+- answering questions about spoken content; and
+- translation of Estonian speech into English.
+
+The output style depends on the instruction. Prompts should state the desired
+task and format explicitly. This is not a certified system for legal,
+medical, safety-critical, or high-stakes transcription.
+
+## Usage with Transformers
+
+The following example uses the selected local checkpoint for Estonian
+transcription:
+
+```python
+import torch
+from transformers import AutoProcessor, VoxtralForConditionalGeneration
+
+model_id = "exp/models/voxtral-mini-3b-2507-full/1c/checkpoint-4500"
+device = "cuda"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = VoxtralForConditionalGeneration.from_pretrained(
+    model_id,
+    dtype=torch.bfloat16,
+    attn_implementation="sdpa",
+    low_cpu_mem_usage=True,
+).to(device)
+model.eval()
+
+inputs = processor.apply_transcription_request(
+    language="et",
+    audio="example.wav",
+    model_id=model_id,
+    return_tensors="pt",
+)
+inputs = {
+    key: value.to(device=device, dtype=torch.bfloat16)
+    if value.is_floating_point()
+    else value.to(device)
+    for key, value in inputs.items()
+}
+
+with torch.inference_mode():
+    output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=8192)
+
+prompt_length = inputs["input_ids"].shape[1]
+text = processor.batch_decode(
+    output_ids[:, prompt_length:], skip_special_tokens=True
+)[0]
+print(text.strip())
+```
+
+Use 16 kHz mono audio where possible. For long recordings, segmenting at
+natural speech boundaries is recommended.
+
+## Training data
+
+The training data was combined from seven task datasets. 
+
+| Task | Input | Retained | Filtered out |
+|---|---:|---:|---:|
+|  Verbatim Estonian ASR | 13,434 | 12,972 | 462 |
+|  Edited broadcast subtitles | 5,639 | 5,412 | 227 |
+| Estonian news stories from audio | 50,000 | 49,998 | 2 |
+|  English news stories from Estonian audio | 10,000 | 9,999 | 1 |
+|  Broadcast summarization and structured extraction | 49,803 | 49,797 | 6 |
+|  Questions about broadcast audio | 10,000 | 5,264 | 4,736 |
+|  Edited parliamentary stenograms | 17,556 | 15,955 | 1,601 |
+| **Total**  | **156,432** | **149,397** | **7,035** |
+
+Of the 7,035 filtered examples, 5,500 exceeded the task-specific audio-duration
+limit and 1,535 exceeded the maximum combined audio/text sequence length.
+Transcription examples were limited to 30 minutes and audio-understanding
+examples to 40 minutes. The final prepared manifest used a maximum sequence
+length of 28,000 tokens.
+
+Combined sequence lengths in the retained manifest were:
+
+| Statistic | Tokens |
+|---|---:|
+| Minimum | 419 |
+| Median | 6,845 |
+| 90th percentile | 19,163 |
+| 95th percentile | 19,734 |
+| 99th percentile | 24,925 |
+| Maximum | 27,999 |
+
+Some  instructions and
+answers in trainining data and the English news-story targets were generated with
+`google/gemini-3.1-flash-lite`. Consequently, the training targets may contain
+errors or stylistic biases inherited from that model.
+
+The source collections include ERR broadcasts and subtitles, an Estonian ASR
+manifest, ERR news video/audio and article text, Päevakaja programmes,
+Riigikogu recordings and stenograms, and
+[`TalTechNLP/qa_broadcast_conv_et`](https://huggingface.co/datasets/TalTechNLP/qa_broadcast_conv_et).
+Users are responsible for checking the terms, copyright, privacy constraints,
+and permitted uses of the underlying data for their application.
+
+## Training procedure
+
+Training used four NVIDIA A100-SXM4 80 GB GPUs. The per-device batch size was
+1 with 8 gradient-accumulation steps, giving an effective global batch size of
+32. Both the audio encoder and aligner were unfrozen.
+
+| Hyperparameter | Value |
+|---|---|
+| Maximum training steps | 6,000 |
+| Selected checkpoint step | 4,500 |
+| Effective global batch size | 32 |
+| Optimizer | Fused AdamW |
+| Learning rate | `5e-5` |
+| Adam betas | `(0.9, 0.95)` |
+| Adam epsilon | `1e-8` |
+| Weight decay | `0.01` |
+| Maximum gradient norm | `1.0` |
+| Scheduler | Cosine |
+| Warmup ratio | `0.03` |
+| Training precision | bfloat16 with TF32 enabled |
+| Gradient checkpointing | Enabled for language and vision/audio components |
+| Sequence packing | Disabled |
+| Truncation strategy | Delete over-length examples |
+| Evaluation/save interval | 500 steps |
+| Random and data seeds | 13 |
+
+The run was resumed from checkpoints during development, and the configured
+maximum sequence length was reduced from 32,768 to 30,000 and finally 28,000.
+The data statistics above describe the final prepared 28,000-token manifest;
+they should not be interpreted as an exact record of the manifest state at
+every earlier resumed stage.
+
+## Evaluation
+
+Checkpoints were ranked by word error rate on an internal held-out Estonian ASR
+set. Generation used greedy decoding (`temperature=0`) with up to 8,192 new
+tokens.
+
+| Checkpoint | WER | Reference words |
+|---:|---:|---:|
+| **4,500** | **5.05%** | 9,085 |
+
+The validation set contains only 10 recordings. These figures are useful for
+checkpoint selection but should not be treated as a broad estimate of Estonian
+ASR quality or as directly comparable with public benchmark results.
+
+## Toolkits and environment
+
+The recorded environment for the final training stage included:
+
+| Toolkit | Version or role |
+|---|---|
+| ms-swift | 4.4.2; supervised fine-tuning and evaluation loop |
+| Transformers | 4.57.6; Voxtral model and processor |
+| PyTorch | 2.10.0 |
+| Datasets | 3.6.0 |
+| Accelerate | 1.12.0 |
+| Mistral Common | 1.11.7 |
+| kaldiio | 2.18.1; reading FLAC `ark` audio |
+| SoundFile | 0.13.1 |
+| Safetensors | 0.7.0 |
+| Weights & Biases | 0.27.2; experiment tracking |
+| CUDA | 13.2 |
+| Slurm | Distributed job launch and resource allocation |
+
+A local ms-swift plugin supplied Voxtral dataset integration and support for
+the repository's `ark:<path>:<offset>` audio references.
+
+## Limitations
+
+- The model can hallucinate, omit, repeat, or incorrectly normalize speech.
+- Performance may degrade for dialects, code-switching, overlapping speakers,
+  noise, distant microphones, music, or domains underrepresented in training.
+- The English outputs were trained on machine-generated references and may
+  inherit translation errors.
+- Generated summaries and answers are not guaranteed to be factually faithful
+  to the recording.
+- Training data may contain personal names, opinions, and other information
+  present in public broadcasts or parliamentary speech. Outputs should be
+  reviewed before publication or consequential use.
+
+## Acknowledgements
+
+The model builds on Mistral AI's
+[`Voxtral Mini 3B 2507`](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507).
+See the upstream model card for architecture details, original capabilities,
+usage requirements, and base-model limitations.