formatting

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "f5-tts"
-version = "0.5.3"
+version = "0.6.0"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}
@@ -25,6 +25,7 @@ dependencies = [
     "jieba",
     "librosa",
     "matplotlib",
+    "nltk",
     "numpy<=1.26.4",
     "pydub",
     "pypinyin",
@@ -40,7 +41,6 @@ dependencies = [
     "vocos",
     "wandb",
     "x_transformers>=1.31.14",
-    "nltk"
 ]
 
 [project.optional-dependencies]

src/f5_tts/infer/README.md

@@ -155,7 +155,8 @@ import time
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-async def listen_to_F5TTS(text, server_ip='localhost', server_port=9998):
+
+async def listen_to_F5TTS(text, server_ip="localhost", server_port=9998):
     client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     await asyncio.get_event_loop().run_in_executor(None, client_socket.connect, (server_ip, int(server_port)))
 
@@ -164,13 +165,9 @@ async def listen_to_F5TTS(text, server_ip='localhost', server_port=9998):
 
     async def play_audio_stream():
         nonlocal first_chunk_time
-        buffer = b''
+        buffer = b""
         p = pyaudio.PyAudio()
-        stream = p.open(format=pyaudio.paFloat32,
-                        channels=1,
-                        rate=24000,
-                        output=True,
-                        frames_per_buffer=2048)
+        stream = p.open(format=pyaudio.paFloat32, channels=1, rate=24000, output=True, frames_per_buffer=2048)
 
         try:
             while True:
@@ -195,7 +192,7 @@ async def listen_to_F5TTS(text, server_ip='localhost', server_port=9998):
         logger.info(f"Total time taken: {time.time() - start_time:.4f} seconds")
 
     try:
-        data_to_send = f"{character_name}|{text}".encode('utf-8')
+        data_to_send = f"{text}".encode("utf-8")
         await asyncio.get_event_loop().run_in_executor(None, client_socket.sendall, data_to_send)
         await play_audio_stream()
 
@@ -205,8 +202,9 @@ async def listen_to_F5TTS(text, server_ip='localhost', server_port=9998):
     finally:
         client_socket.close()
 
+
 if __name__ == "__main__":
-    text_to_send = "As a Reader assistant, I'm familiar with blockchain technology. which are key to its improved performance in terms of both training speed and inference efficiency.Let’s break down the components"
+    text_to_send = "As a Reader assistant, I'm familiar with new technology. which are key to its improved performance in terms of both training speed and inference efficiency.Let's break down the components"
 
     asyncio.run(listen_to_F5TTS(text_to_send))
 ```

src/f5_tts/infer/utils_infer.py

@@ -529,25 +529,26 @@ def infer_batch_process(
 
     return final_wave, target_sample_rate, combined_spectrogram
 
+
 # infer batch process for stream mode
 def infer_batch_process_stream(
-        ref_audio,
-        ref_text,
-        gen_text_batches,
-        model_obj,
-        vocoder,
-        mel_spec_type="vocos",
-        progress=None,
-        target_rms=0.1,
-        cross_fade_duration=0.15,
-        nfe_step=32,
-        cfg_strength=2.0,
-        sway_sampling_coef=-1,
-        speed=1,
-        fix_duration=None,
-        device=None,
-        streaming=False,
-        chunk_size=2048
+    ref_audio,
+    ref_text,
+    gen_text_batches,
+    model_obj,
+    vocoder,
+    mel_spec_type="vocos",
+    progress=None,
+    target_rms=0.1,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    cfg_strength=2.0,
+    sway_sampling_coef=-1,
+    speed=1,
+    fix_duration=None,
+    device=None,
+    streaming=False,
+    chunk_size=2048,
 ):
     audio, sr = ref_audio
     if audio.shape[0] > 1:
@@ -585,7 +586,6 @@ def infer_batch_process_stream(
             gen_text_len = len(gen_text.encode("utf-8"))
             duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / local_speed)
 
-
         with torch.inference_mode():
             generated, _ = model_obj.sample(
                 cond=audio,
@@ -618,7 +618,7 @@ def infer_batch_process_stream(
 
             if streaming:
                 for j in range(0, len(generated_wave), chunk_size):
-                    yield generated_wave[j:j + chunk_size], target_sample_rate
+                    yield generated_wave[j : j + chunk_size], target_sample_rate
 
             return generated_wave, generated_mel_spec[0].cpu().numpy()
 
@@ -629,7 +629,7 @@ def infer_batch_process_stream(
     else:
         with ThreadPoolExecutor() as executor:
             futures = [executor.submit(process_batch, i, gen_text) for i, gen_text in enumerate(gen_text_batches)]
-            for future in (progress.tqdm(futures) if progress else futures):
+            for future in progress.tqdm(futures) if progress else futures:
                 result = future.result()
                 if result:
                     generated_wave, generated_mel_spec = result
@@ -671,6 +671,8 @@ def infer_batch_process_stream(
             yield final_wave, target_sample_rate, combined_spectrogram
         else:
             yield None, target_sample_rate, None
+
+
 # remove silence from generated wav
 
 
@@ -694,4 +696,4 @@ def save_spectrogram(spectrogram, path):
     plt.imshow(spectrogram, origin="lower", aspect="auto")
     plt.colorbar()
     plt.savefig(path)
-    plt.close()
+    plt.close()

src/f5_tts/socket_server.py

@@ -6,7 +6,6 @@ import logging
 import wave
 import numpy as np
 import argparse
-import time
 import traceback
 import gc
 import threading
@@ -20,8 +19,10 @@ from importlib.resources import files
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 class AudioFileWriterThread(threading.Thread):
     """Threaded file writer to avoid blocking the TTS streaming process."""
+
     def __init__(self, output_file, sampling_rate):
         super().__init__()
         self.output_file = output_file
@@ -33,7 +34,7 @@ class AudioFileWriterThread(threading.Thread):
     def run(self):
         """Process queued audio data and write it to a file."""
         logger.info("AudioFileWriterThread started.")
-        with wave.open(self.output_file, 'wb') as wf:
+        with wave.open(self.output_file, "wb") as wf:
             wf.setnchannels(1)
             wf.setsampwidth(2)
             wf.setframerate(self.sampling_rate)
@@ -102,12 +103,19 @@ class TTSStreamingProcessor:
     def _warm_up(self):
         logger.info("Warming up the model...")
         gen_text = "Warm-up text for the model."
-        for _ in infer_batch_process_stream((self.audio, self.sr), self.ref_text, [gen_text], self.model, self.vocoder, device=self.device, streaming=True):
+        for _ in infer_batch_process_stream(
+            (self.audio, self.sr),
+            self.ref_text,
+            [gen_text],
+            self.model,
+            self.vocoder,
+            device=self.device,
+            streaming=True,
+        ):
             pass
         logger.info("Warm-up completed.")
 
     def generate_stream(self, text, conn):
-        start_time = time.time()
         text_batches = sent_tokenize(text)
 
         audio_stream = infer_batch_process_stream(
@@ -118,13 +126,13 @@ class TTSStreamingProcessor:
             self.vocoder,
             device=self.device,
             streaming=True,
-            chunk_size=2048
+            chunk_size=2048,
         )
 
         # Reset the file writer thread
         if self.file_writer_thread is not None:
             self.file_writer_thread.stop()
-        self.file_writer_thread = AudioFileWriterThread('output.wav', self.sampling_rate)
+        self.file_writer_thread = AudioFileWriterThread("output.wav", self.sampling_rate)
         self.file_writer_thread.start()
 
         for audio_chunk, _ in audio_stream:
@@ -132,7 +140,7 @@ class TTSStreamingProcessor:
                 logger.info(f"Generated audio chunk of size: {len(audio_chunk)}")
 
                 # Send audio chunk via socket
-                conn.sendall(struct.pack(f'{len(audio_chunk)}f', *audio_chunk))
+                conn.sendall(struct.pack(f"{len(audio_chunk)}f", *audio_chunk))
 
                 # Write to file asynchronously
                 self.file_writer_thread.add_chunk(audio_chunk)
@@ -165,6 +173,7 @@ def handle_client(conn, processor):
         logger.error(f"Error handling client: {e}")
         traceback.print_exc()
 
+
 def start_server(host, port, processor):
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
         s.bind((host, port))
@@ -175,6 +184,7 @@ def start_server(host, port, processor):
             logger.info(f"Connected by {addr}")
             handle_client(conn, processor)
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()