bugfix

conf/config.json

@@ -5,8 +5,8 @@
 	"remove_silence":false,
 	"modelname":"F5-TTS",
 	"device":"cuda:0",
-	"ref_audio_fn":"$[workdir]$/samples/test_zh_1_ref_short.wav",
+	"ref_audio_fn":"$[workdir]$/samples/ttt.wav",
-	"ref_text":"对，这就是我，万人敬仰的太乙真人。",
+	"ref_text":"快点吃饭，上课要迟到了。",
 	"cross_fade_duration":0
 }
 

f5tts.py

@@ -72,30 +72,22 @@ class F5TTS:
 				ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
 
 		self.model = load_model(model_cls, model_cfg, ckpt_file, 
-						self.config.vocab_file).to(self.config.device)
+						self.config.vocab_file)
+		self.model = self.model.to(self.config.device)
 
 	def generate(self, d):
 		msg= d.decode('utf-8')
 		data = DictObject(**json.loads(msg))
 		print(data)
 		t1 = time()
-		for wav in self.inference(data.prompt, stream=data.stream):
 		if data.stream:
+			for wav in self.inference_stream(data.prompt, stream=data.stream):
 				d = {
 					"reqid":data.reqid,
 					"b64wave":b64str(wav),
 					"finish":False
 				}
 				self.replier.send(json.dumps(d))
-			else:
-				t2 = time()
-				d = {
-					"reqid":data.reqid,
-					"audio_file":wav,
-					"time_cost":t2 - t1
-				}
-				print(f'{d}')
-				return json.dumps(d)
 			t2 = time()
 			d = {
 				"reqid":data.reqid,
@@ -103,6 +95,16 @@ class F5TTS:
 				"finish":True
 			}
 			return json.dumps(d)
+		else:
+			audio_fn = self.inference(data.prompt)
+			t2 = time()
+			d = {
+				"reqid":data.reqid,
+				"audio_file":audio_fn,
+				"time_cost":t2 - t1
+			}
+			print(f'{d}')
+			return json.dumps(d)
 
 	def setup_voice(self):
 		main_voice = {"ref_audio": self.config.ref_audio_fn, 
@@ -121,8 +123,7 @@ class F5TTS:
 			print("Ref_text:", voices[voice]["ref_text"])
 		self.voices = voices
 
-
+	def inference_stream(self, prompt):
-	def inference(self, prompt):
 		text_gen = prompt
 		remove_silence = False
 		generated_audio_segments = []
@@ -146,6 +147,24 @@ class F5TTS:
 			print(f"Voice: {voice}, {self.model}")
 			audio, final_sample_rate, spectragram = \
 					infer_process(ref_audio, ref_text, gen_text, self.model)
+			yield {
+				'audio':audio,
+				'sample_rate':final_sample_rate,
+				'spectragram':spectragram,
+				'finish':False
+			}
+		yield {
+			'finish':True
+		}
+
+	def inference(self, prompt):
+		generated_audio_segments = []
+		remove_silence = self.config.remove_silence or False
+		final_sample_rate = 24000
+		for d in self.inference_stream(prompt):
+			if not d['finish']:
+				audio = d['audio'] 
+				final_sample_rate = d['sample_rate']
 				generated_audio_segments.append(audio)
 
 		if generated_audio_segments:
@@ -158,13 +177,14 @@ class F5TTS:
 					remove_silence_for_generated_wav(f.name)
 			return fn
 
-
 if __name__ == '__main__':
-	# workdir = os.getcwd()
+	workdir = os.getcwd()
-	# config = getConfig(workdir)
+	config = getConfig(workdir, {'workdir':workdir})
+	print(config.ref_audio_fn)
 	tts = F5TTS()
 	print('here')
-	# tts.run()
+	tts.run()
+	"""
 	while True:
 		print('prompt:')
 		p = input()
@@ -173,7 +193,4 @@ if __name__ == '__main__':
 			f = tts.inference(p)
 			t2 = time()
 			print(f'{f}, cost {t2-t1} seconds')
-
+	"""
-
-
-

zmq_client.py

@@ -1,3 +1,4 @@
+import sys
 import json
 import os
 
@@ -9,7 +10,7 @@ from appPublic.uniqueID import getID
 zmq_url = "tcp://127.0.0.1:9999"
 from time import time
 
-class ASRClient:
+class F5TTSClient:
     def __init__(self, zmq_url):
         self.zmq_url = zmq_url
         self.requester = ZmqRequester(self.zmq_url)