init

app.py

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+import librosa
+import gradio as gr
+import numpy as np
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import soundfile as sf
+import torch
+
+# load model and tokenizer
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+def speech2text(audio):
+    sr, data = audio
+
+    # resample to 16hz
+    data_16hz = librosa.resample(data[:,0].astype(np.float32),sr,16000)
+
+    # tokenize
+    input_values = processor([data_16hz], return_tensors="pt", padding="longest").input_values  # Batch size 1
+
+    # retrieve logits
+    logits = model(input_values).logits
+
+    # take argmax and decode
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+
+    return transcription[0]  # batch size 1
+
+iface = gr.Interface(speech2text, "microphone", "text")
+
+iface.launch()
+

requirements.txt

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+gradio
+transformers
+soundfile
+torch
+librosa