Update README.md

README.md

@@ -63,9 +63,9 @@ Shorter audio samples may lead to reduced prediction accuracy.
 
 The model outputs a dictionary of the following form `{"depression":score, "anxiety": score}`.
 
-If `quantized=False` (see the Usage section below), the scores are returned as raw float values which correlate monotonically with PHQ-9 and GAD-7.
+If `quantize=False` (see the Usage section below), the scores are returned as raw float values which correlate monotonically with PHQ-9 and GAD-7.
 
-If `quantized=True` the scores are converted into integers representing the severity of depression and anxiety.
+If `quantize=True` the scores are converted into integers representing the severity of depression and anxiety.
 
 **Quantization levels for depression task:**
 
@@ -101,21 +101,34 @@ If `quantized=True` the scores are converted into integers representing the seve
 # Usage
 1. Checkout the repo:
 
+Make sure the git LFS or XET extensions are installed so that the model checkpoint itself will be downloaded instead of a pointer to it. 
+
 ```
 git clone https://huggingface.co/KintsugiHealth/dam
 ```
 
-2. Install requirements: 
-```python
-pip install -r requirements.txt
+2. Install requirements:
+
+Install the `mamba` package manager, then run
+
+```
+mamba env create -n dam -f requirements.txt
 ```
 
+to create the environment and
+
+```
+mamba activate dam
+```
+
+to activate it.
+
 3. Load and run pipeline
 ```python
 from pipeline import Pipeline
 
 pipeline = Pipeline()
-result = pipeline.run_on_file("sample.wav", quantized=True)
+result = pipeline.run_on_file("sample.wav", quantize=True)
 print(result)
 ```
 The output will resemble a dictionary, for example {'depression': 2, 'anxiety': 3}, indicating that the analyzed audio sample exhibits voice biomarkers consistent with severe depression and severe anxiety.