Cosmobillian
/

turkish_whisper_for_noisy_datas_v1

@@ -1,199 +1,166 @@
----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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
-### 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:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

+# Cosmobillian / turkish_whisper_for_noisy_datas
+> A Whisper-large-v3 model fine-tuned for **noisy Turkish speech recognition** (short utterances, real-world environments).
+---
+## 🔎 Model Summary
+- **Base model:** `openai/whisper-large-v3`
+- **Language:** Turkish (`tr`)
+- **Task:** Automatic Speech Recognition (ASR) – Transcription
+- **Domain:** Noisy / real-world audio (street, phone mic, background noise, reverb, etc.)
+- **Input audio:** mono, 16 kHz, short segments (≈ 3–8 seconds)
+- **Fine-tuning type:** Full model (decoder-focused fine-tuning, encoder frozen)
+This model is designed to perform robust speech-to-text for **noisy Turkish audio**, especially:
+- mobile / cheap microphone recordings
+- mild background music or chatter
+- echo / reverb (rooms, corridors etc.)
+It is **not** a general multilingual model any more; the decoding is heavily biased towards **Turkish**.
+---
+## ✅ Intended Use
+**Primary use-case:**
+- Transcribing **short Turkish utterances** with background noise (e.g. real calls, vlogs, “in the wild” recordings).
+**Good for:**
+- Prototypes of Turkish ASR systems
+- Voice-enabled assistants for Turkish users
+- Noisy datasets (phone, street, public places, YouTube-like content)
+**Not ideal for:**
+- Long-form audio without chunking (podcasts, 1+ minute single shot)
+- High-stakes applications (medical/legal dictation) without manual review
+- Clean studio speech where smaller Whisper models already perform very well
+---
+## ⚙️ Training Details
+> Note: This is a **custom fine-tuned model**; base capabilities come from `openai/whisper-large-v3`.
+- **Base model:** `openai/whisper-large-v3`
+- **Fine-tuned on:** Private Turkish dataset of short (~5s) audio clips
+  - Noisy, real-world conditions
+  - Paired with manually prepared transcriptions
+- **Sampling rate:** 16 kHz, mono
+- **Loss:** Cross-entropy with label smoothing
+- **Strategy:**
+  - Encoder **frozen** (only decoder fine-tuned)
+  - Small learning rate to avoid catastrophic forgetting
+  - Short training (1 epoch) to adapt to noise style while preserving base knowledge
+> Exact dataset is not public; this model should be treated as **research / experimental**.
+---
+## 📊 Evaluation
+The model has been manually checked on several noisy Turkish utterances. Qualitatively:
+- Much more robust to **background noise** than vanilla Whisper on the same custom data
+- Better handling of casual/spontaneous speech (hesitations, filler words, etc.)
+- Occasionally produces grammatically imperfect sentences (as expected from ASR)
+There is **no official WER benchmark** on a public dataset yet (e.g. Common Voice, MLS).
+If you use this model in a paper or product, please:
+- Benchmark on your own dev/test set
+- Share WER / CER numbers if possible 🙏
+---
+## 🚀 Quickstart (Hugging Face `pipeline`)
+```python
+!pip install -q transformers soundfile librosa
+import torch
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+MODEL_ID = "Cosmobillian/turkish_whisper_for_noisy_datas"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+processor = WhisperProcessor.from_pretrained(MODEL_ID)
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID).to(device)
+# Dil/task prompt'unu zorla (TR + transcribe)
+forced_ids = processor.get_decoder_prompt_ids(
+    language="turkish",
+    task="transcribe",
+)
+model.config.forced_decoder_ids = forced_ids
+if hasattr(model, "generation_config"):
+    model.generation_config.forced_decoder_ids = forced_ids
+def load_audio(path, target_sr=16000):
+    audio, sr = librosa.load(path, sr=None, mono=True)
+    if sr != target_sr:
+        audio = librosa.resample(audio, orig_sr=sr, target_sr=target_sr)
+        sr = target_sr
+    return audio, sr
+def chunked_transcribe(path, chunk_sec=30.0, stride_sec=5.0, max_new_tokens=256):
+    speech, sr = load_audio(path, 16000)
+    chunk_size = int(chunk_sec * sr)
+    stride_size = int(stride_sec * sr)
+    texts = []
+    start = 0
+    while start < len(speech):
+        end = start + chunk_size
+        chunk = speech[start:end]
+        if len(chunk) == 0:
+            break
+        inputs = processor(
+            chunk,
+            sampling_rate=sr,
+            return_tensors="pt",
+        )
+        input_features = inputs.input_features.to(device)
+        with torch.no_grad():
+            generated_ids = model.generate(
+                input_features,
+                max_new_tokens=max_new_tokens,
+                do_sample=False,
+                num_beams=1,
+                no_repeat_ngram_size=3,
+                repetition_penalty=1.2,
+            )
+        text = processor.batch_decode(
+            generated_ids,
+            skip_special_tokens=True
+        )[0]
+        texts.append(text)
+        # bir sonraki chunk'a stride kadar kayarak git
+        start = end - stride_size
+    return " ".join(texts)
+# ÖRNEK KULLANIM
+AUDIO_PATH = "/content/uzun_kayit.wav"
+full_text = chunked_transcribe(AUDIO_PATH, chunk_sec=30, stride_sec=5, max_new_tokens=256)
+print("Tam transkripsiyon:\n")
+print(full_text)