Upload f5_tts/socket_server.py with huggingface_hub

f5_tts/socket_server.py

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+import socket
+import struct
+import torch
+import torchaudio
+from threading import Thread
+
+
+import gc
+import traceback
+
+
+from infer.utils_infer import infer_batch_process, preprocess_ref_audio_text, load_vocoder, load_model
+from model.backbones.dit import DiT
+
+
+class TTSStreamingProcessor:
+    def __init__(self, ckpt_file, vocab_file, ref_audio, ref_text, device=None, dtype=torch.float32):
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Load the model using the provided checkpoint and vocab files
+        self.model = load_model(
+            model_cls=DiT,
+            model_cfg=dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
+            ckpt_path=ckpt_file,
+            mel_spec_type="vocos",  # or "bigvgan" depending on vocoder
+            vocab_file=vocab_file,
+            ode_method="euler",
+            use_ema=True,
+            device=self.device,
+        ).to(self.device, dtype=dtype)
+
+        # Load the vocoder
+        self.vocoder = load_vocoder(is_local=False)
+
+        # Set sampling rate for streaming
+        self.sampling_rate = 24000  # Consistency with client
+
+        # Set reference audio and text
+        self.ref_audio = ref_audio
+        self.ref_text = ref_text
+
+        # Warm up the model
+        self._warm_up()
+
+    def _warm_up(self):
+        """Warm up the model with a dummy input to ensure it's ready for real-time processing."""
+        print("Warming up the model...")
+        ref_audio, ref_text = preprocess_ref_audio_text(self.ref_audio, self.ref_text)
+        audio, sr = torchaudio.load(ref_audio)
+        gen_text = "Warm-up text for the model."
+
+        # Pass the vocoder as an argument here
+        infer_batch_process((audio, sr), ref_text, [gen_text], self.model, self.vocoder, device=self.device)
+        print("Warm-up completed.")
+
+    def generate_stream(self, text, play_steps_in_s=0.5):
+        """Generate audio in chunks and yield them in real-time."""
+        # Preprocess the reference audio and text
+        ref_audio, ref_text = preprocess_ref_audio_text(self.ref_audio, self.ref_text)
+
+        # Load reference audio
+        audio, sr = torchaudio.load(ref_audio)
+
+        # Run inference for the input text
+        audio_chunk, final_sample_rate, _ = infer_batch_process(
+            (audio, sr),
+            ref_text,
+            [text],
+            self.model,
+            self.vocoder,
+            device=self.device,  # Pass vocoder here
+        )
+
+        # Break the generated audio into chunks and send them
+        chunk_size = int(final_sample_rate * play_steps_in_s)
+
+        if len(audio_chunk) < chunk_size:
+            packed_audio = struct.pack(f"{len(audio_chunk)}f", *audio_chunk)
+            yield packed_audio
+            return
+
+        for i in range(0, len(audio_chunk), chunk_size):
+            chunk = audio_chunk[i : i + chunk_size]
+
+            # Check if it's the final chunk
+            if i + chunk_size >= len(audio_chunk):
+                chunk = audio_chunk[i:]
+
+            # Send the chunk if it is not empty
+            if len(chunk) > 0:
+                packed_audio = struct.pack(f"{len(chunk)}f", *chunk)
+                yield packed_audio
+
+
+def handle_client(client_socket, processor):
+    try:
+        while True:
+            # Receive data from the client
+            data = client_socket.recv(1024).decode("utf-8")
+            if not data:
+                break
+
+            try:
+                # The client sends the text input
+                text = data.strip()
+
+                # Generate and stream audio chunks
+                for audio_chunk in processor.generate_stream(text):
+                    client_socket.sendall(audio_chunk)
+
+                # Send end-of-audio signal
+                client_socket.sendall(b"END_OF_AUDIO")
+
+            except Exception as inner_e:
+                print(f"Error during processing: {inner_e}")
+                traceback.print_exc()  # Print the full traceback to diagnose the issue
+                break
+
+    except Exception as e:
+        print(f"Error handling client: {e}")
+        traceback.print_exc()
+    finally:
+        client_socket.close()
+
+
+def start_server(host, port, processor):
+    server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    server.bind((host, port))
+    server.listen(5)
+    print(f"Server listening on {host}:{port}")
+
+    while True:
+        client_socket, addr = server.accept()
+        print(f"Accepted connection from {addr}")
+        client_handler = Thread(target=handle_client, args=(client_socket, processor))
+        client_handler.start()
+
+
+if __name__ == "__main__":
+    try:
+        # Load the model and vocoder using the provided files
+        ckpt_file = ""  # pointing your checkpoint "ckpts/model/model_1096.pt"
+        vocab_file = ""  # Add vocab file path if needed
+        ref_audio = ""  # add ref audio"./tests/ref_audio/reference.wav"
+        ref_text = ""
+
+        # Initialize the processor with the model and vocoder
+        processor = TTSStreamingProcessor(
+            ckpt_file=ckpt_file,
+            vocab_file=vocab_file,
+            ref_audio=ref_audio,
+            ref_text=ref_text,
+            dtype=torch.float32,
+        )
+
+        # Start the server
+        start_server("0.0.0.0", 9998, processor)
+    except KeyboardInterrupt:
+        gc.collect()