Update README.md

README.md

@@ -8,114 +8,4 @@ hardware: a100-large
 pinned: false
 ---
 
-# Transformers-library ASR Evaluation
-
-This folder contains evaluation scripts for ASR models supported by the 🤗 Transformers library.
-
-## Supported Models
-
-| Script | Models |
-|--------|--------|
-| `run_whisper.sh` | OpenAI Whisper, Distil-Whisper, CrisperWhisper |
-| `run_wav2vec2.sh` | Wav2Vec2 |
-| `run_wav2vec2_conformer.sh` | Wav2Vec2 Conformer |
-| `run_hubert.sh` | HuBERT |
-| `run_data2vec.sh` | Data2Vec |
-| `run_mms.sh` | MMS |
-| `run_moonshine.sh` | Moonshine, Moonshine Streaming |
-| `run_voxtral.sh` | Voxtral Mini, Voxtral Small |
-| `run_voxtral_realtime.sh` | Voxtral Realtime |
-| `run_vibevoice.sh` | VibeVoice |
-| `run_glm_asr.sh` | GLM-ASR |
-| `run_granite.sh` | Granite Speech |
-
-### Multilingual
-
-| Script | Models |
-|--------|--------|
-| `run_whisper_ml.sh` | OpenAI Whisper (multilingual) |
-| `run_voxtral_ml.sh` | Voxtral Mini, Voxtral Small |
-| `run_voxtral_realtime_ml.sh` | Voxtral Realtime |
-
-Multilingual scripts evaluate on FLEURS, MCV (Mozilla Common Voice), and MLS (Multilingual LibriSpeech) for German, French, Italian, Spanish, and Portuguese. They use `run_eval_ml.py` which applies language-specific normalization. By default, models auto-detect the language during inference as per the leaderboard convention. The argument `--language` can be used to force a specific language.
-
-## Docker usage (recommended)
-
-From the **repository root**, build the Docker image:
-
-```bash
-docker build -t open-asr-transformers -f transformers/Dockerfile .
-```
-
-### Run a specific script directly
-
-From the **repository root**, you can run a script without entering the container. The command below uses `--gpus` to expose all GPUs, mounts the local repo so scripts reflect latest changes, and mounts the HuggingFace cache for model downloads:
-
-```bash
-docker run --gpus all \
-    -v $(pwd):/app \
-    -v $HF_HOME:/root/.cache/huggingface \
-    open-asr-transformers run_whisper.sh
-```
-
-Results are written to `transformers/results/` and are automatically persisted on the host since the repo is mounted.
-
-To select a specific GPU (e.g. GPU 1):
-
-```bash
-docker run --gpus '"device=1"' \
-    -v $(pwd):/app \
-    -v $HF_HOME:/root/.cache/huggingface \
-    open-asr-transformers run_whisper.sh
-```
-
-### Run interactively
-
-From the **repository root**, you can also enter the container to run interactively:
-
-```bash
-docker run --gpus all -it \
-    -v $(pwd):/app \
-    -v $HF_HOME:/root/.cache/huggingface \
-    open-asr-transformers -i
-```
-
-This drops you into a bash shell inside `/app/transformers`. From there, run any evaluation script:
-
-```bash
-# Evaluate all Whisper models
-bash run_whisper.sh
-
-# Evaluate Granite models
-bash run_granite.sh
-
-# Evaluate a single model/dataset manually
-python run_eval.py \
-    --model_id=openai/whisper-large-v3-turbo \
-    --dataset_path="hf-audio/open-asr-leaderboard" \
-    --dataset="librispeech" \
-    --split="test.clean" \
-    --device=0 \
-    --batch_size=64 \
-    --max_eval_samples=-1
-```
-
-### Docker cheat sheet
-
-- Exit and stop a container, type `exit` or press `Ctrl+D`.
-- Detach from a container (without stopping): `Ctrl+P` then `Ctrl+Q`.
-- List running containers: `docker ps -a`.
-- Attach to a container: `docker attach <container_id>`
-- Delete a container: `docker rm <container_id>`
-
-## Local Setup (without Docker)
-
-From the repository root:
-
-```bash
-pip install -r requirements/requirements.txt
-pip install "mistral-common[audio]>=1.9.0"   # only needed for Voxtral
-pip install peft    # for Granite
-cd transformers
-bash run_whisper.sh
-```
+# Dockerfile for evaluation Transformers models