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@@ -27,28 +27,27 @@ Or with Conda:
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  conda install -c conda-forge huggingface_hub
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  ```
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- Now you can download the model by executing the following code:
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-
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- ```
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- from huggingface_hub import hf_hub_download
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-
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- model_file = hf_hub_download(repo_id='Jenthe/ECAPA2', filename='model.pt')
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- ```
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-
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- Subsequent calls will load the previously downloaded model automatically.
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-
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  ### Speaker Embedding Extraction
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  Extracting speaker embeddings is easy and only requires a few lines of code:
 
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  ```
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  import torch
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  import torchaudio
 
 
 
 
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- audio = torchaudio.load('sample.wav')
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  model = torch.jit.load(model_file, map_location='cpu')
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  embedding = model.extract_embedding(audio)
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  ```
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  ### Hierarchical Feature Extraction
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  For the extraction of other hierachical features, a separate model function is provided:
 
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  conda install -c conda-forge huggingface_hub
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  ```
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  ### Speaker Embedding Extraction
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  Extracting speaker embeddings is easy and only requires a few lines of code:
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+
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  ```
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  import torch
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  import torchaudio
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+ from huggingface_hub import hf_hub_download
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+
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+ model_file = hf_hub_download(repo_id='Jenthe/ECAPA2', filename='model.pt')
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+ model = torch.jit.load(model_file, map_location='cpu')
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+ audio, sr = torchaudio.load('sample.wav')
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  model = torch.jit.load(model_file, map_location='cpu')
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  embedding = model.extract_embedding(audio)
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  ```
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+ Subsequent calls will load the previously downloaded model automatically.
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+
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+ Note: input audio should have a sample rate of 16 kHz.
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+
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  ### Hierarchical Feature Extraction
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  For the extraction of other hierachical features, a separate model function is provided: