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app.py

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+import gradio as gr
+import os 
+import numpy as np 
+import librosa
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from transformers import AutoFeatureExtractor
+from sklearnex import patch_sklearn, unpatch_sklearn
+patch_sklearn()
+import xgboost as xgb
+
+
+MAX_DURATION = 2
+# Sampling rate is the number of samples of audio recorded every second
+SAMPLING_RATE = 16000
+BATCH_SIZE = 2  # Batch-size for training and evaluating our model.
+NUM_CLASSES = 8  # Number of classes our dataset will have (11 in our case).
+HIDDEN_DIM = 768  # Dimension of our model output (768 in case of Wav2Vec 2.0 - Base).
+MAX_SEQ_LENGTH = MAX_DURATION * SAMPLING_RATE  # Maximum length of the input audio file.
+# Wav2Vec 2.0 results in an output frequency with a stride of about 20ms.
+MAX_FRAMES = 99
+MAX_EPOCHS = 5  # Maximum number of training epochs.
+RAVDESS_CLASS_LABELS = ("angry", "calm", "disgust", "fear", "happy", "neutral","sad","surprise")
+MODEL_CHECKPOINT = "facebook/wav2vec2-base" 
+
+labels = RAVDESS_CLASS_LABELS
+label2id, id2label = dict(), dict()
+
+from transformers import TFWav2Vec2Model
+
+
+def mean_pool(hidden_states, feature_lengths):
+    attenion_mask = tf.sequence_mask(
+        feature_lengths, maxlen=MAX_FRAMES, dtype=tf.dtypes.int64
+    )
+    padding_mask = tf.cast(
+        tf.reverse(tf.cumsum(tf.reverse(attenion_mask, [-1]), -1), [-1]),
+        dtype=tf.dtypes.bool,
+    )
+    hidden_states = tf.where(
+        tf.broadcast_to(
+            tf.expand_dims(~padding_mask, -1), (BATCH_SIZE, MAX_FRAMES, HIDDEN_DIM)
+        ),
+        0.0,
+        hidden_states,
+    )
+    pooled_state = tf.math.reduce_sum(hidden_states, axis=1) / tf.reshape(
+        tf.math.reduce_sum(tf.cast(padding_mask, dtype=tf.dtypes.float32), axis=1),
+        [-1, 1],
+    )
+    return pooled_state
+
+
+class TFWav2Vec2ForAudioClassification(keras.Model):
+
+    def __init__(self, model_checkpoint):
+        super().__init__()
+        # Instantiate the Wav2Vec 2.0 model without the Classification-Head
+        self.wav2vec2 = TFWav2Vec2Model.from_pretrained(
+            model_checkpoint, apply_spec_augment=False, from_pt=True
+        )
+        self.pooling = layers.GlobalAveragePooling1D()
+        self.flat = layers.Flatten()
+        self.intermediate_layer_dropout = layers.Dropout(0.5)
+      
+
+    def call(self, inputs):
+        hidden_states = self.wav2vec2(inputs[0])[0]
+        if tf.is_tensor(inputs[1]):
+            audio_lengths = tf.cumsum(inputs[1], -1)[:, -1]
+            feature_lengths = self.wav2vec2.wav2vec2._get_feat_extract_output_lengths(
+                audio_lengths
+            )
+            pooled_state = mean_pool(hidden_states, feature_lengths)
+        else:
+            pooled_state = self.pooling(hidden_states)
+
+        intermediate_state = self.flat(self.intermediate_layer_dropout(pooled_state))
+
+        return intermediate_state
+
+wav2vec2_model = TFWav2Vec2ForAudioClassification(MODEL_CHECKPOINT)
+wav2vec2_model.load_weights('model.keras')
+
+for i, label in enumerate(labels):
+    label2id[label] = str(i)
+    id2label[str(i)] = label
+
+
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    MODEL_CHECKPOINT, return_attention_mask=True
+)
+xgb_params = {
+    'objective':                    'binary:logistic',
+    'predictor':                    'cpu_predictor',
+    'disable_default_eval_metric':  'true',
+}
+
+model_xgb= xgb.XGBClassifier(**xgb_params)
+
+def greet(name):
+  inp =  feature_extractor(
+    name[1],
+    sampling_rate=feature_extractor.sampling_rate,
+    max_length=MAX_SEQ_LENGTH,
+    truncation=True,
+    padding=True,
+  )
+  inp = np.array([y for x,y in inp.items()])
+  pred = wav2vec2_model.predict([inp[0],inp[1]])
+  pred = model_xgb.predict(pred)
+  lab = id2label[str(pred[0])]
+  return lab
+
+iface = gr.Interface(fn=greet, inputs="audio", outputs="text")
+iface.launch()

requirements.txt

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+transformers
+datasets
+huggingface-hub
+joblib
+librosa
+resampy
+tensorflow
+sklearnex
+keras