modify, test=doc

demos/speech_server/cls_client.sh

+#!/bin/bash
+
+wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
+paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav --topk 1

docs/source/released_model.md

@@ -69,7 +69,7 @@ GE2E + FastSpeech2 | AISHELL-3  |[ge2e-fastspeech2-aishell3](https://github.com/
 
 Model Type | Dataset| Example Link | Pretrained Models | Static Models 
 :-------------:| :------------:| :-----: | :-----: | :-----:
-PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz) 
+PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz)(18M), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz)(19M), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz)(289M) 
 PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)
 
 ## Punctuation Restoration Models

paddlespeech/server/bin/paddlespeech_client.py

@@ -70,13 +70,10 @@ class TTSClientExecutor(BaseExecutor):
             choices=[0, 8000, 16000],
             help='Sampling rate, the default is the same as the model')
         self.parser.add_argument(
-            '--output',
-            type=str,
-            default="./output.wav",
-            help='Synthesized audio file')
+            '--output', type=str, default=None, help='Synthesized audio file')
 
-    def postprocess(self, response_dict: dict, outfile: str) -> float:
-        wav_base64 = response_dict["result"]["audio"]
+    def postprocess(self, wav_base64: str, outfile: str) -> float:
+        #wav_base64 = response_dict["result"]["audio"]
         audio_data_byte = base64.b64decode(wav_base64)
         # from byte
         samples, sample_rate = soundfile.read(
@@ -93,37 +90,38 @@ class TTSClientExecutor(BaseExecutor):
         else:
             logger.error("The format for saving audio only supports wav or pcm")
 
-        duration = len(samples) / sample_rate
-        return duration
-
     def execute(self, argv: List[str]) -> bool:
         args = self.parser.parse_args(argv)
-        try:
-            url = 'http://' + args.server_ip + ":" + str(
-                args.port) + '/paddlespeech/tts'
-            request = {
-                "text": args.input,
-                "spk_id": args.spk_id,
-                "speed": args.speed,
-                "volume": args.volume,
-                "sample_rate": args.sample_rate,
-                "save_path": args.output
-            }
-            st = time.time()
-            response = requests.post(url, json.dumps(request))
-            time_consume = time.time() - st
-
-            response_dict = response.json()
-            duration = self.postprocess(response_dict, args.output)
+        input_ = args.input
+        server_ip = args.server_ip
+        port = args.port
+        spk_id = args.spk_id
+        speed = args.speed
+        volume = args.volume
+        sample_rate = args.sample_rate
+        output = args.output
 
+        try:
+            time_start = time.time()
+            res = self(
+                input=input_,
+                server_ip=server_ip,
+                port=port,
+                spk_id=spk_id,
+                speed=speed,
+                volume=volume,
+                sample_rate=sample_rate,
+                output=output)
+            time_end = time.time()
+            time_consume = time_end - time_start
+            response_dict = res.json()
             logger.info(response_dict["message"])
-            logger.info("Save synthesized audio successfully on %s." %
-                        (args.output))
-            logger.info("Audio duration: %f s." % (duration))
+            logger.info("Save synthesized audio successfully on %s." % (output))
+            logger.info("Audio duration: %f s." %
+                        (response_dict['result']['duration']))
             logger.info("Response time: %f s." % (time_consume))
-
             return True
-        except BaseException:
+        except Exception as e:
             logger.error("Failed to synthesized audio.")
             return False
 
@@ -136,7 +134,7 @@ class TTSClientExecutor(BaseExecutor):
                  speed: float=1.0,
                  volume: float=1.0,
                  sample_rate: int=0,
-                 output: str="./output.wav"):
+                 output: str=None):
         """
         Python API to call an executor.
         """
@@ -151,20 +149,11 @@ class TTSClientExecutor(BaseExecutor):
             "save_path": output
         }
 
-        try:
-            st = time.time()
-            response = requests.post(url, json.dumps(request))
-            time_consume = time.time() - st
-            response_dict = response.json()
-            duration = self.postprocess(response_dict, output)
-
-            print(response_dict["message"])
-            print("Save synthesized audio successfully on %s." % (output))
-            print("Audio duration: %f s." % (duration))
-            print("Response time: %f s." % (time_consume))
-            print("RTF: %f " % (time_consume / duration))
-        except BaseException:
-            print("Failed to synthesized audio.")
+        res = requests.post(url, json.dumps(request))
+        response_dict = res.json()
+        if not output:
+            self.postprocess(response_dict["result"]["audio"], output)
+        return res
 
 
 @cli_client_register(
@@ -193,24 +182,27 @@ class ASRClientExecutor(BaseExecutor):
 
     def execute(self, argv: List[str]) -> bool:
         args = self.parser.parse_args(argv)
-        url = 'http://' + args.server_ip + ":" + str(
-            args.port) + '/paddlespeech/asr'
-        audio = wav2base64(args.input)
-        data = {
-            "audio": audio,
-            "audio_format": args.audio_format,
-            "sample_rate": args.sample_rate,
-            "lang": args.lang,
-        }
-        time_start = time.time()
+        input_ = args.input
+        server_ip = args.server_ip
+        port = args.port
+        sample_rate = args.sample_rate
+        lang = args.lang
+        audio_format = args.audio_format
+
         try:
-            r = requests.post(url=url, data=json.dumps(data))
-            # ending Timestamp
+            time_start = time.time()
+            res = self(
+                input=input_,
+                server_ip=server_ip,
+                port=port,
+                sample_rate=sample_rate,
+                lang=lang,
+                audio_format=audio_format)
             time_end = time.time()
-            logger.info(r.json())
-            logger.info("time cost %f s." % (time_end - time_start))
+            logger.info(res.json())
+            logger.info("Response time %f s." % (time_end - time_start))
             return True
-        except BaseException:
+        except Exception as e:
             logger.error("Failed to speech recognition.")
             return False
 
@@ -234,15 +226,9 @@ class ASRClientExecutor(BaseExecutor):
             "sample_rate": sample_rate,
             "lang": lang,
         }
-        time_start = time.time()
-        try:
-            r = requests.post(url=url, data=json.dumps(data))
-            # ending Timestamp
-            time_end = time.time()
-            print(r.json())
-            print("time cost %f s." % (time_end - time_start))
-        except BaseException:
-            print("Failed to speech recognition.")
+
+        res = requests.post(url=url, data=json.dumps(data))
+        return res
 
 
 @cli_client_register(
@@ -270,22 +256,19 @@ class CLSClientExecutor(BaseExecutor):
 
     def execute(self, argv: List[str]) -> bool:
         args = self.parser.parse_args(argv)
-        url = 'http://' + args.server_ip + ":" + str(
-            args.port) + '/paddlespeech/cls'
-        audio = wav2base64(args.input)
-        data = {
-            "audio": audio,
-            "topk": args.topk,
-        }
-        time_start = time.time()
+        input_ = args.input
+        server_ip = args.server_ip
+        port = args.port
+        topk = args.topk
+
         try:
-            r = requests.post(url=url, data=json.dumps(data))
-            # ending Timestamp
+            time_start = time.time()
+            res = self(input=input_, server_ip=server_ip, port=port, topk=topk)
             time_end = time.time()
-            logger.info(r.json())
+            logger.info(res.json())
             logger.info("Response time %f s." % (time_end - time_start))
             return True
-        except BaseException:
+        except Exception as e:
             logger.error("Failed to speech classification.")
             return False
 
@@ -302,12 +285,6 @@ class CLSClientExecutor(BaseExecutor):
         url = 'http://' + server_ip + ":" + str(port) + '/paddlespeech/cls'
         audio = wav2base64(input)
         data = {"audio": audio, "topk": topk}
-        time_start = time.time()
-        try:
-            r = requests.post(url=url, data=json.dumps(data))
-            # ending Timestamp
-            time_end = time.time()
-            print(r.json())
-            print("Response time %f s." % (time_end - time_start))
-        except BaseException:
-            print("Failed to speech classification.")
+
+        res = requests.post(url=url, data=json.dumps(data))
+        return res

paddlespeech/server/engine/tts/paddleinference/tts_engine.py

@@ -531,4 +531,4 @@ class TTSEngine(BaseEngine):
                                                            postprocess_time))
         logger.info("RTF: {}".format(rtf))
 
-        return lang, target_sample_rate, wav_base64
+        return lang, target_sample_rate, duration, wav_base64

paddlespeech/server/engine/tts/python/tts_engine.py

@@ -250,4 +250,4 @@ class TTSEngine(BaseEngine):
         logger.info("RTF: {}".format(rtf))
         logger.info("device: {}".format(self.device))
 
-        return lang, target_sample_rate, wav_base64
+        return lang, target_sample_rate, duration, wav_base64

paddlespeech/server/restful/response.py

@@ -54,10 +54,11 @@ class ASRResponse(BaseModel):
 #****************************************************************************************/
 class TTSResult(BaseModel):
     lang: str = "zh"
-    sample_rate: int
     spk_id: int = 0
     speed: float = 1.0
     volume: float = 1.0
+    sample_rate: int
+    duration: float
     save_path: str = None
     audio: str
 

paddlespeech/server/restful/tts_api.py

@@ -98,7 +98,7 @@ def tts(request_body: TTSRequest):
         tts_engine = engine_pool['tts']
         logger.info("Get tts engine successfully.")
 
-        lang, target_sample_rate, wav_base64 = tts_engine.run(
+        lang, target_sample_rate, duration, wav_base64 = tts_engine.run(
             text, spk_id, speed, volume, sample_rate, save_path)
 
         response = {
@@ -113,6 +113,7 @@ def tts(request_body: TTSRequest):
                 "speed": speed,
                 "volume": volume,
                 "sample_rate": target_sample_rate,
+                "duration": duration,
                 "save_path": save_path,
                 "audio": wav_base64
             }