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Server Py update

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This commit is contained in:
GamerBoss101
2025-03-29 21:18:19 -04:00
parent 5da627097d
commit 99a6c7d413
1 changed files with 73 additions and 52 deletions
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109
Backend/server.py
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@@ -52,24 +52,31 @@ manager = ConnectionManager()
# Helper function to convert audio data
async def decode_audio_data(audio_data: str) -> torch.Tensor:
"""Decode base64 audio data to a torch tensor"""
# Decode base64 audio data
binary_data = base64.b64decode(audio_data.split(',')[1] if ',' in audio_data else audio_data)
try:
# Decode base64 audio data
binary_data = base64.b64decode(audio_data.split(',')[1] if ',' in audio_data else audio_data)
# Load audio from binary data
buf = BytesIO(binary_data)
audio_tensor, sample_rate = torchaudio.load(buf)
# Save to a temporary WAV file first
temp_file = BytesIO(binary_data)
# Resample if needed
if sample_rate != generator.sample_rate:
audio_tensor = torchaudio.functional.resample(
audio_tensor.squeeze(0),
orig_freq=sample_rate,
new_freq=generator.sample_rate
)
else:
audio_tensor = audio_tensor.squeeze(0)
# Load audio from binary data, explicitly specifying the format
audio_tensor, sample_rate = torchaudio.load(temp_file, format="wav")
return audio_tensor
# Resample if needed
if sample_rate != generator.sample_rate:
audio_tensor = torchaudio.functional.resample(
audio_tensor.squeeze(0),
orig_freq=sample_rate,
new_freq=generator.sample_rate
)
else:
audio_tensor = audio_tensor.squeeze(0)
return audio_tensor
except Exception as e:
print(f"Error decoding audio: {str(e)}")
# Return a small silent audio segment as fallback
return torch.zeros(generator.sample_rate // 2) # 0.5 seconds of silence
async def encode_audio_data(audio_tensor: torch.Tensor) -> str:
@@ -95,43 +102,57 @@ async def websocket_endpoint(websocket: WebSocket):
action = request.get("action")
if action == "generate":
text = request.get("text", "")
speaker_id = request.get("speaker", 0)
try:
text = request.get("text", "")
speaker_id = request.get("speaker", 0)
# Generate audio response
print(f"Generating audio for: '{text}' with speaker {speaker_id}")
audio_tensor = generator.generate(
text=text,
speaker=speaker_id,
context=context_segments,
max_audio_length_ms=10_000,
)
# Generate audio response
print(f"Generating audio for: '{text}' with speaker {speaker_id}")
audio_tensor = generator.generate(
text=text,
speaker=speaker_id,
context=context_segments,
max_audio_length_ms=10_000,
)
# Add to conversation context
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
# Add to conversation context
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
# Convert audio to base64 and send back to client
audio_base64 = await encode_audio_data(audio_tensor)
await websocket.send_json({
"type": "audio_response",
"audio": audio_base64
})
# Convert audio to base64 and send back to client
audio_base64 = await encode_audio_data(audio_tensor)
await websocket.send_json({
"type": "audio_response",
"audio": audio_base64
})
except Exception as e:
print(f"Error generating audio: {str(e)}")
await websocket.send_json({
"type": "error",
"message": f"Error generating audio: {str(e)}"
})
elif action == "add_to_context":
text = request.get("text", "")
speaker_id = request.get("speaker", 0)
audio_data = request.get("audio", "")
try:
text = request.get("text", "")
speaker_id = request.get("speaker", 0)
audio_data = request.get("audio", "")
# Convert received audio to tensor
audio_tensor = await decode_audio_data(audio_data)
# Convert received audio to tensor
audio_tensor = await decode_audio_data(audio_data)
# Add to conversation context
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
# Add to conversation context
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
await websocket.send_json({
"type": "context_updated",
"message": "Audio added to context"
})
await websocket.send_json({
"type": "context_updated",
"message": "Audio added to context"
})
except Exception as e:
print(f"Error adding to context: {str(e)}")
await websocket.send_json({
"type": "error",
"message": f"Error processing audio: {str(e)}"
})
elif action == "clear_context":
context_segments = []