adicionando requirements e app

app.py

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+import gradio as gr
+import speech_recognition as sr
+from pydub import AudioSegment
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import os
+import torch
+
+tokenizer = Wav2Vec2Processor.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')
+model = Wav2Vec2ForCTC.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')
+# Load the pre-trained speech recognition model
+recognizer = sr.Recognizer()
+
+def recognize_speech(audio_path):
+    print(audio_path)
+    # Perform speech recognition on the captured audio
+    try:
+        clip = AudioSegment.from_file(audio_path)
+        clip = clip.set_frame_rate(16000)
+        print(clip)
+        x = torch.FloatTensor(clip.get_array_of_samples())
+        inputs = tokenizer(x, sampling_rate=16000, return_tensors='pt', padding='longest').input_values
+        logits = model(inputs).logits
+        tokens = torch.argmax(logits, axis=-1)
+        text = tokenizer.batch_decode(tokens)
+        return str(text).lower()
+    except sr.UnknownValueError:
+        return "Could not understand the audio."
+    except sr.RequestError as e:
+        return f"Error accessing the Google Speech Recognition service: {e}"
+
+# Create the Gradio interface with microphone input
+audio_recognizer_interface = gr.Interface(
+    fn=recognize_speech,
+    inputs=gr.inputs.Audio(source="microphone", type="filepath", label="Speak into the microphone..."),
+    outputs="text",
+    title="Real-time Speech Recognition"
+)
+
+# Run the interface
+audio_recognizer_interface.launch()

requirements.txt

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+speech_recognition
+pydub
+transformers
+torch