added temp sine wave to test base64 encoding

handler.py

@@ -4,6 +4,43 @@ from transformers import AutoProcessor, MusicgenForConditionalGeneration
 import torch
 import io
 import base64
+import wave
+import array
+import math
+
+def generate_sine_wave(freq, duration, sample_rate, amplitude):
+    n_samples = int(sample_rate * duration)
+    samples = []
+    
+    for x in range(n_samples):
+        value = amplitude * math.sin(2 * math.pi * freq * x / sample_rate)
+        samples.append(int(value))  # rounding to the nearest integer
+    
+    return array.array("h", samples)  # array of short integers
+
+
+def sine_to_base64():
+    frequency = 440.0  # Frequency in Hz
+    duration = 1.0     # seconds
+    volume = 0.5       # 0.0 to 1.0
+    sample_rate = 44100
+    amplitude = int(volume * 32767)  # 16-bit audio
+    
+    sine_wave = generate_sine_wave(frequency, duration, sample_rate, amplitude)
+    
+    wav_buffer = io.BytesIO()
+    with wave.open(wav_buffer, "w") as wav_file:
+        n_channels = 1
+        sampwidth = 2
+        n_frames = len(sine_wave)
+        comptype = "NONE"
+        compname = "not compressed"
+        wav_file.setparams((n_channels, sampwidth, int(sample_rate), n_frames, comptype, compname))
+        wav_file.writeframes(sine_wave.tobytes())
+    
+    base64_string = base64.b64encode(wav_buffer.getvalue()).decode('utf-8')
+    return base64_string
+
 
 def create_params(params, fr):
     # default
@@ -38,7 +75,7 @@ class EndpointHandler:
         self.model = MusicgenForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16)
         self.model.to('cuda')
 
-    def __call__(self, data: Dict[str, Any]) -> str:
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, str]]:
         """
         Args:
             data (:dict:):
@@ -47,37 +84,37 @@ class EndpointHandler:
        Returns: wav file in bytes
         """
 
-        inputs = data.pop("inputs", data)
-        params = data.pop("parameters", None)
+        # inputs = data.pop("inputs", data)
+        # params = data.pop("parameters", None)
 
-        inputs = self.processor(
-            text=[inputs],
-            padding=True,
-            return_tensors="pt"
-        ).to('cuda')
+        # inputs = self.processor(
+        #     text=[inputs],
+        #     padding=True,
+        #     return_tensors="pt"
+        # ).to('cuda')
 
-        params = create_params(params, self.model.config.audio_encoder.frame_rate)
+        # params = create_params(params, self.model.config.audio_encoder.frame_rate)
 
-        with torch.cuda.amp.autocast():
-            outputs = self.model.generate(**inputs, **params)
+        # with torch.cuda.amp.autocast():
+        #     outputs = self.model.generate(**inputs, **params)
 
-        pred = outputs[0, 0].cpu().numpy()
-        sr = 32000
+        # pred = outputs[0, 0].cpu().numpy()
+        # sr = 32000
 
-        try:
-             sr = self.model.config.audio_encoder.sampling_rate
+        # try:
+        #      sr = self.model.config.audio_encoder.sampling_rate
 
-        except:
-             sr = 32000
+        # except:
+        #      sr = 32000
 
-        wav_buffer = io.BytesIO()
-        wavfile.write(wav_buffer, rate=sr, data=pred)
+        # wav_buffer = io.BytesIO()
+        # wavfile.write(wav_buffer, rate=sr, data=pred)
+        # wav_data = wav_buffer.getvalue()
 
-        wav_data = wav_buffer.getvalue()
-        base64_encoded_wav = base64.b64encode(wav_data).decode('utf-8')
+        # base64_encoded_wav = base64.b64encode(wav_data).decode('utf-8')
 
-        # will return the audio in base64 and client must request with accept: image/jpeg and decode the base64
-        return  base64_encoded_wav
+        base64_encoded_wav = sine_to_base64()
+        return  [{"audio": base64_encoded_wav}]
 
 
 if __name__ == "__main__":