Update README.md

README.md

@@ -19,19 +19,63 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the google/fleurs dataset.
 
-## Model description
+# to run
+simply install chocolatey run this on your cmd:
+```
+@"%SystemRoot%\System32\WindowsPowerShell\v1.0\powershell.exe" -NoProfile -InputFormat None -ExecutionPolicy Bypass -Command "[System.Net.ServicePointManager]::SecurityProtocol = 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))" && SET "PATH=%PATH%;%ALLUSERSPROFILE%\chocolatey\bin"
+```
+# after that install ffmpeg in your device using choco install by running this on cmd after:
+```
+choco install ffmpeg
+```
+# install dependencies in python IDE using:
+```
+pip install --upgrade pip
 
-More information needed
+pip install --upgrade git+https://github.com/huggingface/transformers.git accelerate datasets[audio]
+```
 
-## Intended uses & limitations
+# then lastly to inference the model:
+```
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
-More information needed
 
-## Training and evaluation data
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-More information needed
+model_id = "washeed/audio-transcribe"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+result = pipe("audio.mp3")
+print(result["text"])
+```
+
+# if you want to transcribe instead of translating just  replace the :
+ 
+```result = pipe("audio.mp3")```
+# with
+``` result = pipe("inference.mp3", generate_kwargs={"task": "transcribe"})```
 
-## Training procedure
 
 ### Training hyperparameters