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README.md

@@ -31,16 +31,46 @@ model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config)
 
 path = "/my/path/to/audio.wav"
 outputs = model(path)
-cands = outputs["cands"][0]
-print(cands)
+candidate = outputs["cands"][0]
+print(candidate)
 ```
 
-## Single model performance
+The model can also accept several audio files at the same time (list[str]), or a list of pre-loaded audio files (list[Tensor]). IN this second case you also need to provide the sampling rate of this files:
+
+```py
+import torchaudio
+
+path_1 = "/my/path/to/audio_1.wav"
+path_2 = "/my/path/to/audio_2.wav"
+
+audio_1, sr_1 = torchaudio.load(path_1)
+audio_2, sr_2 = torchaudio.load(path_2)
+
+outputs = model([audio_1, audio_2], sr=[sr_1, sr_2])
+candidates = outputs["cands"]
+print(candidates)
+```
+
+The model can also produces different captions using a Task Embedding input which indicates the dataset caption style. The default task is "clotho".
+
+```py
+outputs = model(path, task="clotho")
+candidate = outputs["cands"][0]
+print(candidate)
+
+outputs = model(path, task="audiocaps")
+candidate = outputs["cands"][0]
+print(candidate)
+```
+
+## Performance
 | Dataset | SPIDEr (%) | SPIDEr-FL (%) | FENSE (%) |
 | ------------- | ------------- | ------------- | ------------- |
 | AudioCaps | 44.14 | 43.98 | 60.81 |
 | Clotho | 30.97 | 30.87 | 51.72 |
 
+This model checkpoint has been trained for the Clotho dataset, but it can also reach a good performance on AudioCaps with the "audiocaps" task.
+
 ## Citation
 The preprint version of the paper describing CoNeTTE is available on arxiv: https://arxiv.org/pdf/2309.00454.pdf
 
@@ -60,6 +90,6 @@ The preprint version of the paper describing CoNeTTE is available on arxiv: http
 ## Additional information
 
 The encoder part of the architecture is based on a ConvNeXt model for audio classification, available here: https://huggingface.co/topel/ConvNeXt-Tiny-AT.
-The encoder weights used are named "convnext_tiny_465mAP_BL_AC_70kit.pth", available on Zenodo: https://zenodo.org/record/8020843.
+More precisely, the encoder weights used are named "convnext_tiny_465mAP_BL_AC_70kit.pth", available on Zenodo: https://zenodo.org/record/8020843.
 
 It was created by [@Labbeti](https://hf.co/Labbeti).