Spaces:
Running
Running
| import json | |
| import re | |
| import requests | |
| import os | |
| import time | |
| import random | |
| import string | |
| from requests_toolbelt.multipart.encoder import MultipartEncoder | |
| absolute_path = os.path.dirname(__file__) | |
| base_url = "http://127.0.0.1:23456" | |
| # 映射表 | |
| def voice_speakers(): | |
| url = f"{base_url}/voice/speakers" | |
| res = requests.post(url=url) | |
| json = res.json() | |
| for i in json: | |
| print(i) | |
| for j in json[i]: | |
| print(j) | |
| return json | |
| # 语音合成 voice vits | |
| def voice_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| save_audio=True, | |
| save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size) | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/vits" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size), | |
| "streaming": 'True' | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size), | |
| "streaming": 'True' | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice" | |
| res = requests.post(url=url, data=m, headers=headers, stream=True) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| audio = res.content | |
| def get_file_size_from_bytes(byte_data): | |
| file_size_offset = 4 | |
| file_size_length = 4 | |
| try: | |
| file_size_bytes = byte_data[file_size_offset:file_size_offset + file_size_length] | |
| file_size = int.from_bytes(file_size_bytes, byteorder='little') | |
| return file_size + 8 | |
| except IndexError: | |
| return None | |
| audio = None | |
| p = 0 | |
| audio_size = None | |
| audios = [] | |
| for chunk in res.iter_content(chunk_size=1024): | |
| if audio is None: | |
| audio = chunk | |
| else: | |
| audio += chunk | |
| p += len(chunk) | |
| if audio_size is not None: | |
| if p >= audio_size: | |
| p = p - audio_size | |
| audios.append(audio[:audio_size]) | |
| audio = audio[audio_size:] | |
| audio_size = get_file_size_from_bytes(audio) | |
| else: | |
| audio_size = get_file_size_from_bytes(audio) | |
| for i, audio in enumerate(audios): | |
| with open(f"{path[:-4]}-{i}.wav", "wb") as f: | |
| f.write(audio) | |
| print(f"{path[:-4]}-{i}.wav") | |
| return path | |
| # 语音转换 hubert-vits | |
| def voice_hubert_vits(upload_path, id, format="wav", length=1, noise=0.667, noisew=0.8, save_audio=True, | |
| save_path=None): | |
| upload_name = os.path.basename(upload_path) | |
| upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg | |
| with open(upload_path, 'rb') as upload_file: | |
| fields = { | |
| "upload": (upload_name, upload_file, upload_type), | |
| "id": str(id), | |
| "format": format, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/hubert-vits" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # 维度情感模型 w2v2-vits | |
| def voice_w2v2_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| emotion=0, | |
| save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size), | |
| "emotion": str(emotion) | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/w2v2-vits" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # 语音转换 同VITS模型内角色之间的音色转换 | |
| def voice_conversion(upload_path, original_id, target_id, save_audio=True, save_path=None): | |
| upload_name = os.path.basename(upload_path) | |
| upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg | |
| with open(upload_path, 'rb') as upload_file: | |
| fields = { | |
| "upload": (upload_name, upload_file, upload_type), | |
| "original_id": str(original_id), | |
| "target_id": str(target_id), | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/conversion" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def voice_ssml(ssml, save_audio=True, save_path=None): | |
| fields = { | |
| "ssml": ssml, | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/ssml" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def voice_dimensional_emotion(upload_path, save_audio=True, | |
| save_path=None): | |
| upload_name = os.path.basename(upload_path) | |
| upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg | |
| with open(upload_path, 'rb') as upload_file: | |
| fields = { | |
| "upload": (upload_name, upload_file, upload_type), | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/dimension-emotion" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def vits_json(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size) | |
| } | |
| f = json.dumps(fields) | |
| url = f"{base_url}/voice" | |
| header = {"Content-Type": 'application/json'} | |
| res = requests.post(url=url, data=f, headers=header) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # Bert_vits2 | |
| def voice_bert_vits2(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50, | |
| sdp_ratio=0.2, save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "length": str(length), | |
| "noise": str(noise), | |
| "noisew": str(noisew), | |
| "segment_size": str(segment_size), | |
| "sdp_ratio": str(sdp_ratio) | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/bert-vits2" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # gpt_sovits | |
| def voice_gpt_sovits(text, id=0, format="wav", lang="auto", preset=None, prompt_text=None, prompt_lang="auto", | |
| segment_size=50, reference_audio=None, save_audio=True, save_path=None): | |
| upload_name, upload_type, upload_file = None, None, None | |
| if reference_audio is not None: | |
| upload_name = os.path.basename(reference_audio) | |
| upload_type = f'audio/{upload_name.split(".")[1]}' | |
| with open(reference_audio, 'rb') as f: | |
| upload_file = f.read() | |
| fields = { | |
| "text": text, | |
| "id": str(id), | |
| "format": format, | |
| "lang": lang, | |
| "segment_size": str(segment_size), | |
| "preset": preset, | |
| "reference_audio": (upload_name, upload_file, upload_type) if reference_audio else None, | |
| "prompt_text": prompt_text, | |
| "prompt_lang": prompt_lang | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/gpt-sovits" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # Reading | |
| def voice_reading_get(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None, | |
| save_audio=True, save_path=None): | |
| res = requests.get( | |
| url=f"{base_url}/voice/reading?text={text}&in_model_type={in_model_type}&in_id={in_id}&preset={preset}&nr_model_type={nr_model_type}&nr_id={nr_id}&lang={lang}&format={format}") | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # Reading | |
| def voice_reading_json(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None, | |
| save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "in_model_type": in_model_type, | |
| "in_id": str(in_id), | |
| "nr_model_type": nr_model_type, | |
| "nr_id": str(nr_id), | |
| "format": format, | |
| "lang": lang, | |
| } | |
| f = json.dumps(fields) | |
| url = f"{base_url}/voice/reading" | |
| header = {"Content-Type": 'application/json'} | |
| res = requests.post(url=url, data=f, headers=header) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| # Reading | |
| def voice_reading(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None, | |
| save_audio=True, save_path=None): | |
| fields = { | |
| "text": text, | |
| "in_model_type": in_model_type, | |
| "in_id": str(in_id), | |
| "nr_model_type": nr_model_type, | |
| "nr_id": str(nr_id), | |
| "format": format, | |
| "lang": lang, | |
| } | |
| boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) | |
| m = MultipartEncoder(fields=fields, boundary=boundary) | |
| headers = {"Content-Type": m.content_type} | |
| url = f"{base_url}/voice/reading" | |
| res = requests.post(url=url, data=m, headers=headers) | |
| fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] | |
| if save_path is not None: | |
| path = os.path.join(save_path, fname) | |
| else: | |
| path = os.path.join(absolute_path, fname) | |
| if save_audio: | |
| with open(path, "wb") as f: | |
| f.write(res.content) | |
| print(path) | |
| return path | |
| return None | |
| def test_interface(text): | |
| error_num = 0 | |
| for i in range(100): | |
| try: | |
| time.sleep(1) | |
| t1 = time.time() | |
| voice_vits(text, format="wav", lang="zh", save_audio=False) | |
| t2 = time.time() | |
| print(f"{i}:len:{len(text)}耗时:{t2 - t1}") | |
| except Exception as e: | |
| error_num += 1 | |
| print(e) | |
| print(f"error_num={error_num}") | |
| if __name__ == '__main__': | |
| cache_path = os.path.join(os.path.curdir, "cache") | |
| text = "你好,こんにちは" | |
| ssml = """ | |
| <speak lang="zh" format="mp3" length="1.2"> | |
| <voice id="0" model_type="GPT-SOVITS" preset="default">这几天心里颇不宁静。</voice> | |
| <voice id="0" model_type="Bert-VITS2">今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。</voice> | |
| <voice id="142">月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；</voice> | |
| <voice id="0" model_type="Bert-VITS2">妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。</voice> | |
| <voice id="120">我悄悄地披了大衫，带上门出去。</voice><break time="2s"/> | |
| <voice id="121">沿着荷塘，是一条曲折的小煤屑路。</voice> | |
| <voice id="122">这是一条幽僻的路；白天也少人走，夜晚更加寂寞。</voice> | |
| <voice id="123">荷塘四面，长着许多树，蓊蓊郁郁的。</voice> | |
| <voice id="124">路的一旁，是些杨柳，和一些不知道名字的树。</voice> | |
| <voice id="125">没有月光的晚上，这路上阴森森的，有些怕人。</voice> | |
| <voice id="126">今晚却很好，虽然月光也还是淡淡的。</voice><break time="2s"/> | |
| <voice id="127">路上只我一个人，背着手踱着。</voice> | |
| <voice id="128">这一片天地好像是我的；我也像超出了平常的自己，到了另一个世界里。</voice> | |
| <voice id="129">我爱热闹，也爱冷静；<break strength="x-weak"/>爱群居，也爱独处。</voice> | |
| <voice id="130">像今晚上，一个人在这苍茫的月下，什么都可以想，什么都可以不想，便觉是个自由的人。</voice> | |
| <voice id="131">白天里一定要做的事，一定要说的话，现在都可不理。</voice> | |
| <voice id="132">这是独处的妙处，我且受用这无边的荷香月色好了。</voice> | |
| </speak> | |
| """ | |
| # path = voice_vits(text, save_path=cache_path) | |
| # path =voice_vits_streaming(text, save_path=cache_path) | |
| # path = voice_w2v2_vits(text, save_path=cache_path) | |
| # path = voice_conversion(path, 1, 3, save_path=cache_path) | |
| # path = voice_hubert_vits(path, 0, save_path=cache_path) | |
| # path = voice_dimensional_emotion(path, save_path=cache_path) | |
| # path = voice_ssml(ssml, save_path=cache_path) | |
| # path = voice_bert_vits2("你好", lang="zh", save_path=cache_path) | |
| # path = voice_bert_vits2("こんにちは", lang="ja", save_path=cache_path) | |
| # path = voice_gpt_sovits(text=text, id=2, preset="wz") | |
| # path = voice_gpt_sovits(text=text, id=2, reference_audio=r"H:\git\vits-simple-api\data\reference_audio\wz_10068.wav",prompt_text="……嗯……大概、快上课的时候开始的。到这个程度的话，……半个小时吧？") | |
| # os.system(path) | |
| # text = "你好“你的修炼速度有些出乎我的意料”" | |
| # path = voice_reading_json(text=text, in_model_type="GPT-SOVITS", preset="wz", in_id=2, nr_model_type="BERT-VITS2", | |
| # nr_id=0) | |
| # os.system(path) | |