Update README.md

README.md

@@ -1,10 +1,10 @@
-This model is fine tuned on the IEMOCAP_speaker_indpt_Ses05F_Ses05M.pickle dataset, which use Ses05F as validation speaker and Ses05M as test speaker. **We do not apply volume normalization, neither data augmentation.** So it is a speaker independent model.
+This model is fine tuned on the IEMOCAP dataset. We applied volume normalization and data augmentation (noise injection, pitch shift and audio stretching). Also, this is a speaker independent model: We use Ses05F in the IEMOCAP dataset as validation speaker and Ses05M as test speaker. 
 
-The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), **without frustration**. No audio augmentation is applied. Fine-tune dataset audios are also not padded or trimed to fixed length. The length setting is done when fine tuning the transoformer using max_length = 8 sec in the feature extractor. 
+The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), *without frustration*. The audios are either padded or trimed to 8-sec-long before fine tuning. 
 
 After **10** epoches of training, the validation accuracy is around **67%**.
 
-In order to impliment this model: run the following code in a python script:
+In order to impliment this model: Please run the following code in a python script:
 
 ```
 from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
@@ -12,8 +12,7 @@ import librosa
 import torch
 
 target_sampling_rate = 16000
-model_name = 'canlinzhang/Sorenson_fine_tune_wav2vec2-on_IEMOCAP_no_aug_no_fru_2'
-my_token = my_token
+model_name = 'canlinzhang/wav2vec2_speech_emotion_recognition_trained_on_IEMOCAP'
 audio_path = your_audio_path
 
 #build id and label dicts    
@@ -22,7 +21,7 @@ label2id = {'neu':0, 'ang':1, 'sad':2, 'hap':3}
 
 feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
 
-model = AutoModelForAudioClassification.from_pretrained(model_name, use_auth_token = my_token)
+model = AutoModelForAudioClassification.from_pretrained(model_name)
 
 y_ini, sr_ini = librosa.load(audio_path, sr=target_sampling_rate)