diff --git a/README.md b/README.md index a08c593..7fd8cd8 100644 --- a/README.md +++ b/README.md @@ -167,7 +167,7 @@ docker build -t cosyvoice:v1.0 . docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity" cd grpc && python3 client.py --port 50000 --mode # for fastapi usage -docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && MODEL_DIR=iic/CosyVoice-300M fastapi dev --port 50000 server.py && sleep infinity" +docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity" cd fastapi && python3 client.py --port 50000 --mode ``` diff --git a/cosyvoice/bin/export_jit.py b/cosyvoice/bin/export_jit.py index 1eceb1d..cbd0f18 100644 --- a/cosyvoice/bin/export_jit.py +++ b/cosyvoice/bin/export_jit.py @@ -44,7 +44,7 @@ def main(): torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) - cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_trt=False) + cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False) # 1. export llm text_encoder llm_text_encoder = cosyvoice.model.llm.text_encoder.half() @@ -60,5 +60,12 @@ def main(): script = torch.jit.optimize_for_inference(script) script.save('{}/llm.llm.fp16.zip'.format(args.model_dir)) + # 3. export flow encoder + flow_encoder = cosyvoice.model.flow.encoder + script = torch.jit.script(flow_encoder) + script = torch.jit.freeze(script) + script = torch.jit.optimize_for_inference(script) + script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir)) + if __name__ == '__main__': main() diff --git a/cosyvoice/bin/export_onnx.py b/cosyvoice/bin/export_onnx.py new file mode 100644 index 0000000..58b5ab6 --- /dev/null +++ b/cosyvoice/bin/export_onnx.py @@ -0,0 +1,109 @@ +# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com) +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +import onnxruntime +import random +import torch +from tqdm import tqdm +from cosyvoice.cli.cosyvoice import CosyVoice + + +def get_dummy_input(batch_size, seq_len, out_channels, device): + x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device) + mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + t = torch.rand((batch_size), dtype=torch.float32, device=device) + spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device) + cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + return x, mask, mu, t, spks, cond + + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False) + + # 1. export flow decoder estimator + estimator = cosyvoice.model.flow.decoder.estimator + + device = cosyvoice.model.device + batch_size, seq_len = 1, 256 + out_channels = cosyvoice.model.flow.decoder.estimator.out_channels + x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device) + torch.onnx.export( + estimator, + (x, mask, mu, t, spks, cond), + '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), + export_params=True, + opset_version=18, + do_constant_folding=True, + input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'], + output_names=['estimator_out'], + dynamic_axes={ + 'x': {0: 'batch_size', 2: 'seq_len'}, + 'mask': {0: 'batch_size', 2: 'seq_len'}, + 'mu': {0: 'batch_size', 2: 'seq_len'}, + 'cond': {0: 'batch_size', 2: 'seq_len'}, + 't': {0: 'batch_size'}, + 'spks': {0: 'batch_size'}, + 'estimator_out': {0: 'batch_size', 2: 'seq_len'}, + } + ) + + # 2. test computation consistency + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] + estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), sess_options=option, providers=providers) + + for _ in tqdm(range(10)): + x, mask, mu, t, spks, cond = get_dummy_input(random.randint(1, 6), random.randint(16, 512), out_channels, device) + output_pytorch = estimator(x, mask, mu, t, spks, cond) + ort_inputs = { + 'x': x.cpu().numpy(), + 'mask': mask.cpu().numpy(), + 'mu': mu.cpu().numpy(), + 't': t.cpu().numpy(), + 'spks': spks.cpu().numpy(), + 'cond': cond.cpu().numpy() + } + output_onnx = estimator_onnx.run(None, ort_inputs)[0] + torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4) + +if __name__ == "__main__": + main() diff --git a/cosyvoice/bin/export_trt.py b/cosyvoice/bin/export_trt.py deleted file mode 100644 index e6d480c..0000000 --- a/cosyvoice/bin/export_trt.py +++ /dev/null @@ -1,8 +0,0 @@ -# TODO 跟export_jit一样的逻辑，完成flow部分的estimator的onnx导出。 -# tensorrt的安装方式，再这里写一下步骤提示如下，如果没有安装，那么不要执行这个脚本，提示用户先安装，不给选择 -try: - import tensorrt -except ImportError: - print('step1, 下载\n step2. 解压，安装whl，') -# 安装命令里tensosrt的根目录用环境变量导入，比如os.environ['tensorrt_root_dir']/bin/exetrace，然后python里subprocess里执行导出命令 -# 后面我会在run.sh里写好执行命令 tensorrt_root_dir=xxxx python cosyvoice/bin/export_trt.py --model_dir xxx \ No newline at end of file diff --git a/cosyvoice/cli/cosyvoice.py b/cosyvoice/cli/cosyvoice.py index 49fe15f..5e1ea9c 100644 --- a/cosyvoice/cli/cosyvoice.py +++ b/cosyvoice/cli/cosyvoice.py @@ -13,6 +13,7 @@ # limitations under the License. import os import time +from tqdm import tqdm from hyperpyyaml import load_hyperpyyaml from modelscope import snapshot_download from cosyvoice.cli.frontend import CosyVoiceFrontEnd @@ -21,7 +22,7 @@ from cosyvoice.utils.file_utils import logging class CosyVoice: - def __init__(self, model_dir, load_jit=True): + def __init__(self, model_dir, load_jit=True, load_onnx=True): instruct = True if '-Instruct' in model_dir else False self.model_dir = model_dir if not os.path.exists(model_dir): @@ -41,7 +42,10 @@ class CosyVoice: '{}/hift.pt'.format(model_dir)) if load_jit: self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir), - '{}/llm.llm.fp16.zip'.format(model_dir)) + '{}/llm.llm.fp16.zip'.format(model_dir), + '{}/flow.encoder.fp32.zip'.format(model_dir)) + if load_onnx: + self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir)) del configs def list_avaliable_spks(self): @@ -49,7 +53,7 @@ class CosyVoice: return spks def inference_sft(self, tts_text, spk_id, stream=False): - for i in self.frontend.text_normalize(tts_text, split=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): model_input = self.frontend.frontend_sft(i, spk_id) start_time = time.time() logging.info('synthesis text {}'.format(i)) @@ -61,7 +65,7 @@ class CosyVoice: def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False): prompt_text = self.frontend.text_normalize(prompt_text, split=False) - for i in self.frontend.text_normalize(tts_text, split=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k) start_time = time.time() logging.info('synthesis text {}'.format(i)) @@ -74,7 +78,7 @@ class CosyVoice: def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False): if self.frontend.instruct is True: raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir)) - for i in self.frontend.text_normalize(tts_text, split=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k) start_time = time.time() logging.info('synthesis text {}'.format(i)) @@ -88,7 +92,7 @@ class CosyVoice: if self.frontend.instruct is False: raise ValueError('{} do not support instruct inference'.format(self.model_dir)) instruct_text = self.frontend.text_normalize(instruct_text, split=False) - for i in self.frontend.text_normalize(tts_text, split=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text) start_time = time.time() logging.info('synthesis text {}'.format(i)) diff --git a/cosyvoice/cli/model.py b/cosyvoice/cli/model.py index 99ccbe5..a78ded4 100644 --- a/cosyvoice/cli/model.py +++ b/cosyvoice/cli/model.py @@ -18,7 +18,7 @@ import time from contextlib import nullcontext import uuid from cosyvoice.utils.common import fade_in_out - +import numpy as np class CosyVoiceModel: @@ -60,11 +60,22 @@ class CosyVoiceModel: self.hift.load_state_dict(torch.load(hift_model, map_location=self.device)) self.hift.to(self.device).eval() - def load_jit(self, llm_text_encoder_model, llm_llm_model): + def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model): llm_text_encoder = torch.jit.load(llm_text_encoder_model) self.llm.text_encoder = llm_text_encoder llm_llm = torch.jit.load(llm_llm_model) self.llm.llm = llm_llm + flow_encoder = torch.jit.load(flow_encoder_model) + self.flow.encoder = flow_encoder + + def load_onnx(self, flow_decoder_estimator_model): + import onnxruntime + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] + del self.flow.decoder.estimator + self.flow.decoder.estimator = onnxruntime.InferenceSession(flow_decoder_estimator_model, sess_options=option, providers=providers) def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): with self.llm_context: @@ -169,4 +180,5 @@ class CosyVoiceModel: self.llm_end_dict.pop(this_uuid) self.mel_overlap_dict.pop(this_uuid) self.hift_cache_dict.pop(this_uuid) - torch.cuda.synchronize() + if torch.cuda.is_available(): + torch.cuda.synchronize() diff --git a/cosyvoice/flow/decoder.py b/cosyvoice/flow/decoder.py index 4349279..be063d3 100755 --- a/cosyvoice/flow/decoder.py +++ b/cosyvoice/flow/decoder.py @@ -159,7 +159,7 @@ class ConditionalDecoder(nn.Module): _type_: _description_ """ - t = self.time_embeddings(t) + t = self.time_embeddings(t).to(t.dtype) t = self.time_mlp(t) x = pack([x, mu], "b * t")[0] diff --git a/cosyvoice/flow/flow.py b/cosyvoice/flow/flow.py index 8cbf013..2d0a730 100644 --- a/cosyvoice/flow/flow.py +++ b/cosyvoice/flow/flow.py @@ -113,7 +113,7 @@ class MaskedDiffWithXvec(torch.nn.Module): # concat text and prompt_text token_len1, token_len2 = prompt_token.shape[1], token.shape[1] token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len - mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(embedding) + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) token = self.input_embedding(torch.clamp(token, min=0)) * mask # text encode diff --git a/cosyvoice/flow/flow_matching.py b/cosyvoice/flow/flow_matching.py index f82eaae..7e31177 100755 --- a/cosyvoice/flow/flow_matching.py +++ b/cosyvoice/flow/flow_matching.py @@ -50,7 +50,7 @@ class ConditionalCFM(BASECFM): shape: (batch_size, n_feats, mel_timesteps) """ z = torch.randn_like(mu) * temperature - t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device) + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) if self.t_scheduler == 'cosine': t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond) @@ -71,16 +71,17 @@ class ConditionalCFM(BASECFM): cond: Not used but kept for future purposes """ t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + t = t.unsqueeze(dim=0) # I am storing this because I can later plot it by putting a debugger here and saving it to a file # Or in future might add like a return_all_steps flag sol = [] for step in range(1, len(t_span)): - dphi_dt = self.estimator(x, mask, mu, t, spks, cond) + dphi_dt = self.forward_estimator(x, mask, mu, t, spks, cond) # Classifier-Free Guidance inference introduced in VoiceBox if self.inference_cfg_rate > 0: - cfg_dphi_dt = self.estimator( + cfg_dphi_dt = self.forward_estimator( x, mask, torch.zeros_like(mu), t, torch.zeros_like(spks) if spks is not None else None, @@ -96,6 +97,21 @@ class ConditionalCFM(BASECFM): return sol[-1] + def forward_estimator(self, x, mask, mu, t, spks, cond): + if isinstance(self.estimator, torch.nn.Module): + return self.estimator.forward(x, mask, mu, t, spks, cond) + else: + ort_inputs = { + 'x': x.cpu().numpy(), + 'mask': mask.cpu().numpy(), + 'mu': mu.cpu().numpy(), + 't': t.cpu().numpy(), + 'spks': spks.cpu().numpy(), + 'cond': cond.cpu().numpy() + } + output = self.estimator.run(None, ort_inputs)[0] + return torch.tensor(output, dtype=x.dtype, device=x.device) + def compute_loss(self, x1, mask, mu, spks=None, cond=None): """Computes diffusion loss diff --git a/cosyvoice/hifigan/generator.py b/cosyvoice/hifigan/generator.py index fd61834..b640219 100644 --- a/cosyvoice/hifigan/generator.py +++ b/cosyvoice/hifigan/generator.py @@ -340,7 +340,7 @@ class HiFTGenerator(nn.Module): s = self._f02source(f0) # use cache_source to avoid glitch - if cache_source.shape[2] == 0: + if cache_source.shape[2] != 0: s[:, :, :cache_source.shape[2]] = cache_source s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) diff --git a/examples/libritts/cosyvoice/run.sh b/examples/libritts/cosyvoice/run.sh index 96eca9b..386e9e4 100644 --- a/examples/libritts/cosyvoice/run.sh +++ b/examples/libritts/cosyvoice/run.sh @@ -102,4 +102,10 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then --deepspeed_config ./conf/ds_stage2.json \ --deepspeed.save_states model+optimizer done +fi + +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir fi \ No newline at end of file diff --git a/examples/magicdata-read/cosyvoice/run.sh b/examples/magicdata-read/cosyvoice/run.sh index 0cf6f6d..0a080ac 100644 --- a/examples/magicdata-read/cosyvoice/run.sh +++ b/examples/magicdata-read/cosyvoice/run.sh @@ -102,4 +102,10 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then --deepspeed_config ./conf/ds_stage2.json \ --deepspeed.save_states model+optimizer done +fi + +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir fi \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index c7a7f7d..4189c5f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,6 +15,7 @@ matplotlib==3.7.5 modelscope==1.15.0 networkx==3.1 omegaconf==2.3.0 +onnx==1.16.0 onnxruntime-gpu==1.16.0; sys_platform == 'linux' onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows' openai-whisper==20231117 @@ -25,6 +26,7 @@ soundfile==0.12.1 tensorboard==2.14.0 torch==2.0.1 torchaudio==2.0.2 +uvicorn==0.30.0 wget==3.2 fastapi==0.111.0 fastapi-cli==0.0.4 diff --git a/runtime/python/fastapi/client.py b/runtime/python/fastapi/client.py index cf32092..981c7c1 100644 --- a/runtime/python/fastapi/client.py +++ b/runtime/python/fastapi/client.py @@ -1,56 +1,68 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import argparse import logging import requests +import torch +import torchaudio +import numpy as np -def saveResponse(path, response): - # 以二进制写入模式打开文件 - with open(path, 'wb') as file: - # 将响应的二进制内容写入文件 - file.write(response.content) def main(): - api = args.api_base + url = "http://{}:{}/inference_{}".format(args.host, args.port, args.mode) if args.mode == 'sft': - url = api + "/api/inference/sft" - payload={ - 'tts': args.tts_text, - 'role': args.spk_id - } - response = requests.request("POST", url, data=payload) - saveResponse(args.tts_wav, response) - elif args.mode == 'zero_shot': - url = api + "/api/inference/zero-shot" - payload={ - 'tts': args.tts_text, - 'prompt': args.prompt_text - } - files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))] - response = requests.request("POST", url, data=payload, files=files) - saveResponse(args.tts_wav, response) - elif args.mode == 'cross_lingual': - url = api + "/api/inference/cross-lingual" - payload={ - 'tts': args.tts_text, - } - files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))] - response = requests.request("POST", url, data=payload, files=files) - saveResponse(args.tts_wav, response) - else: - url = api + "/api/inference/instruct" payload = { - 'tts': args.tts_text, - 'role': args.spk_id, - 'instruct': args.instruct_text + 'tts_text': args.tts_text, + 'spk_id': args.spk_id } - response = requests.request("POST", url, data=payload) - saveResponse(args.tts_wav, response) - logging.info("Response save to {}", args.tts_wav) + response = requests.request("GET", url, data=payload, stream=True) + elif args.mode == 'zero_shot': + payload = { + 'tts_text': args.tts_text, + 'prompt_text': args.prompt_text + } + files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))] + response = requests.request("GET", url, data=payload, files=files, stream=True) + elif args.mode == 'cross_lingual': + payload = { + 'tts_text': args.tts_text, + } + files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))] + response = requests.request("GET", url, data=payload, files=files, stream=True) + else: + payload = { + 'tts_text': args.tts_text, + 'spk_id': args.spk_id, + 'instruct_text': args.instruct_text + } + response = requests.request("GET", url, data=payload, stream=True) + tts_audio = b'' + for r in response.iter_content(chunk_size=16000): + tts_audio += r + tts_speech = torch.from_numpy(np.array(np.frombuffer(tts_audio, dtype=np.int16))).unsqueeze(dim=0) + logging.info('save response to {}'.format(args.tts_wav)) + torchaudio.save(args.tts_wav, tts_speech, target_sr) + logging.info('get response') if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--api_base', + parser.add_argument('--host', type=str, - default='http://127.0.0.1:6006') + default='0.0.0.0') + parser.add_argument('--port', + type=int, + default='50000') parser.add_argument('--mode', default='sft', choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'], diff --git a/runtime/python/fastapi/server.py b/runtime/python/fastapi/server.py index b670665..c540b47 100644 --- a/runtime/python/fastapi/server.py +++ b/runtime/python/fastapi/server.py @@ -1,119 +1,77 @@ -# Set inference model -# export MODEL_DIR=pretrained_models/CosyVoice-300M-Instruct -# For development -# fastapi dev --port 6006 fastapi_server.py -# For production deployment -# fastapi run --port 6006 fastapi_server.py - +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import os import sys -import io,time -from fastapi import FastAPI, Response, File, UploadFile, Form -from fastapi.responses import HTMLResponse -from fastapi.middleware.cors import CORSMiddleware #引入 CORS中间件模块 -from contextlib import asynccontextmanager ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append('{}/../../..'.format(ROOT_DIR)) sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR)) -from cosyvoice.cli.cosyvoice import CosyVoice -from cosyvoice.utils.file_utils import load_wav -import numpy as np -import torch -import torchaudio +import argparse import logging logging.getLogger('matplotlib').setLevel(logging.WARNING) +from fastapi import FastAPI, UploadFile, Form, File +from fastapi.responses import StreamingResponse +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +import numpy as np +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav -class LaunchFailed(Exception): - pass - -@asynccontextmanager -async def lifespan(app: FastAPI): - model_dir = os.getenv("MODEL_DIR", "pretrained_models/CosyVoice-300M-SFT") - if model_dir: - logging.info("MODEL_DIR is {}", model_dir) - app.cosyvoice = CosyVoice(model_dir) - # sft usage - logging.info("Avaliable speakers {}", app.cosyvoice.list_avaliable_spks()) - else: - raise LaunchFailed("MODEL_DIR environment must set") - yield - -app = FastAPI(lifespan=lifespan) - -#设置允许访问的域名 -origins = ["*"] #"*"，即为所有,也可以改为允许的特定ip。 +app = FastAPI() +# set cross region allowance app.add_middleware( - CORSMiddleware, - allow_origins=origins, #设置允许的origins来源 + CORSMiddleware, + allow_origins=["*"], allow_credentials=True, - allow_methods=["*"], # 设置允许跨域的http方法，比如 get、post、put等。 - allow_headers=["*"]) #允许跨域的headers，可以用来鉴别来源等作用。 + allow_methods=["*"], + allow_headers=["*"]) -def buildResponse(output): - buffer = io.BytesIO() - torchaudio.save(buffer, output, 22050, format="wav") - buffer.seek(0) - return Response(content=buffer.read(-1), media_type="audio/wav") +def generate_data(model_output): + for i in model_output: + tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes() + yield tts_audio -@app.post("/api/inference/sft") -@app.get("/api/inference/sft") -async def sft(tts: str = Form(), role: str = Form()): - start = time.process_time() - output = app.cosyvoice.inference_sft(tts, role) - end = time.process_time() - logging.info("infer time is {} seconds", end-start) - return buildResponse(output['tts_speech']) +@app.get("/inference_sft") +async def inference_sft(tts_text: str = Form(), spk_id: str = Form()): + model_output = cosyvoice.inference_sft(tts_text, spk_id) + return StreamingResponse(generate_data(model_output)) -@app.post("/api/inference/zero-shot") -async def zeroShot(tts: str = Form(), prompt: str = Form(), audio: UploadFile = File()): - start = time.process_time() - prompt_speech = load_wav(audio.file, 16000) - prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes() - prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0) - prompt_speech_16k = prompt_speech_16k.float() / (2**15) +@app.get("/inference_zero_shot") +async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()): + prompt_speech_16k = load_wav(prompt_wav.file, 16000) + model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k) + return StreamingResponse(generate_data(model_output)) - output = app.cosyvoice.inference_zero_shot(tts, prompt, prompt_speech_16k) - end = time.process_time() - logging.info("infer time is {} seconds", end-start) - return buildResponse(output['tts_speech']) +@app.get("/inference_cross_lingual") +async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()): + prompt_speech_16k = load_wav(prompt_wav.file, 16000) + model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k) + return StreamingResponse(generate_data(model_output)) -@app.post("/api/inference/cross-lingual") -async def crossLingual(tts: str = Form(), audio: UploadFile = File()): - start = time.process_time() - prompt_speech = load_wav(audio.file, 16000) - prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes() - prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0) - prompt_speech_16k = prompt_speech_16k.float() / (2**15) +@app.get("/inference_instruct") +async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()): + model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text) + return StreamingResponse(generate_data(model_output)) - output = app.cosyvoice.inference_cross_lingual(tts, prompt_speech_16k) - end = time.process_time() - logging.info("infer time is {} seconds", end-start) - return buildResponse(output['tts_speech']) - -@app.post("/api/inference/instruct") -@app.get("/api/inference/instruct") -async def instruct(tts: str = Form(), role: str = Form(), instruct: str = Form()): - start = time.process_time() - output = app.cosyvoice.inference_instruct(tts, role, instruct) - end = time.process_time() - logging.info("infer time is {} seconds", end-start) - return buildResponse(output['tts_speech']) - -@app.get("/api/roles") -async def roles(): - return {"roles": app.cosyvoice.list_avaliable_spks()} - -@app.get("/", response_class=HTMLResponse) -async def root(): - return """ - - - - - Api information - - - Get the supported tones from the Roles API first, then enter the tones and textual content in the TTS API for synthesis. Documents of API - - - """ +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--port', + type=int, + default=50000) + parser.add_argument('--model_dir', + type=str, + default='iic/CosyVoice-300M', + help='local path or modelscope repo id') + args = parser.parse_args() + cosyvoice = CosyVoice(args.model_dir) + uvicorn.run(app, host="127.0.0.1", port=args.port) \ No newline at end of file