Adding example of post audio buffer to transcription backend

examples/whisper/transcription_client.py

@@ -0,0 +1,46 @@
+import sys
+import pyaudio
+import numpy as np
+import wave
+import useful_transformers as rkut
+import requests
+
+
+
+CHANNELS = 1
+FORMAT = pyaudio.paInt16
+RATE = 16000
+RECORD_SECONDS = 5
+# CHUNK_SIZE = RATE * RECORD_SECONDS
+CHUNK_SIZE = 1024
+
+p = pyaudio.PyAudio()
+
+stream = p.open(format=FORMAT, channels=CHANNELS,
+                rate=RATE, input=True,
+                frames_per_buffer=CHUNK_SIZE)
+print("recording...")
+frames = []
+fulldata = np.empty(RATE*RECORD_SECONDS*CHANNELS)
+
+for i in range(0, int(RATE / CHUNK_SIZE * RECORD_SECONDS)):
+    data = stream.read(CHUNK_SIZE)
+    numpydata = np.frombuffer(data, dtype='int16')
+    frames.append(numpydata)
+print("finished recording")
+
+combin_array = np.concatenate(frames).tobytes()
+url = "http://127.0.0.1:5000/api/transcription"
+response = requests.post(url, data=combin_array)
+
+# Check the response
+if response.status_code == 200:
+    result = response.json()
+    print(result)
+else:
+    print('Error:', response.status_code)
+
+stream.stop_stream()
+stream.close()
+p.terminate()
+

examples/whisper/transcription_server.py

@@ -0,0 +1,23 @@
+from flask import Flask, request, jsonify
+import numpy as np
+from .whisper import decode_pcm
+
+app = Flask(__name__)
+
+@app.route('/api/transcription', methods=['POST'])
+def consume_buffer():
+    # Receive the audio buffer as NumPy array from the request
+    array_data = np.frombuffer(request.data, dtype='int16')
+
+    # Decode the buffer 
+    result = buffer_decode(array_data)
+
+    # Return the result as a JSON response
+    return jsonify(result=result)
+
+def buffer_decode(array):
+    text = decode_pcm(array, "tiny.en")
+    return {'message': text}
+
+if __name__ == '__main__':
+    app.run()