Update README.md

README.md

@@ -1,58 +1,58 @@
----
-license: apache-2.0
----
-
-This is a d-Matrix functional reference of the whisper-medium model.
-The reference provides the following functional *configurations*:
-  Configuration | Explanation
-  :-- | :-- 
-  **`BASELINE`** | a reference functionally equivalent to the original model
-  **`BASIC`** | all linear algebraic operands quantized to `MXINT8-64`, and all other operations transformed to approximated kernel simulations
-
-
-### Usage
-
-Install d-Matrix [Dmx_Compressor](https://github.com/d-matrix-ai/dmx-compressor) first.
-```sh
-pip install dmx_compressor
-```
-
-The following is an example model and its evaluation.
-
-```python
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-from datasets import load_dataset
-from dmx.compressor.modeling import DmxModel
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-model_id = "d-matrix/whisper-medium"
-
-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,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
-sample = dataset[0]["audio"]
-shorter_audio = sample["array"][:1000]
-
-pipe.model = DmxModel.from_torch(pipe.model)
-
-result = pipe(shorter_audio)
-print(result["text"])
+---
+license: apache-2.0
+---
+
+This is a d-Matrix functional reference of the whisper-medium model.
+The reference provides the following functional *configurations*:
+  Configuration | Explanation
+  :-- | :-- 
+  **`BASELINE`** | a reference functionally equivalent to the original model
+  **`BASIC`** | all linear algebraic operands quantized to `MXINT8-64`
+
+
+### Usage
+
+Install d-Matrix [Dmx_Compressor](https://github.com/d-matrix-ai/dmx-compressor) first.
+```sh
+pip install dmx_compressor
+```
+
+The following is an example model and its evaluation.
+
+```python
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from datasets import load_dataset
+from dmx.compressor.modeling import DmxModel
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "d-matrix/whisper-medium"
+
+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,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+sample = dataset[0]["audio"]
+shorter_audio = sample["array"][:1000]
+
+pipe.model = DmxModel.from_torch(pipe.model)
+
+result = pipe(shorter_audio)
+print(result["text"])
 ```