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# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
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# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import requests
import soundfile as sf
import json
import numpy as np
import argparse
def get_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--server-url",
type=str,
default="localhost:8000",
help="Address of the server",
)
parser.add_argument(
"--reference-audio",
type=str,
default="../../example/prompt_audio.wav",
help="Path to a single audio file. It can't be specified at the same time with --manifest-dir",
)
parser.add_argument(
"--reference-text",
type=str,
default="吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。",
help="",
)
parser.add_argument(
"--target-text",
type=str,
default="身临其境，换新体验。塑造开源语音合成新范式，让智能语音更自然。",
help="",
)
parser.add_argument(
"--model-name",
type=str,
default="spark_tts",
choices=[
"f5_tts", "spark_tts"
],
help="triton model_repo module name to request: transducer for k2, attention_rescoring for wenet offline, streaming_wenet for wenet streaming, infer_pipeline for paraformer large offline",
)
parser.add_argument(
"--output-audio",
type=str,
default="output.wav",
help="Path to save the output audio",
)
return parser.parse_args()
def prepare_request(
waveform,
reference_text,
target_text,
sample_rate=16000,
padding_duration: int = None,
audio_save_dir: str = "./",
):
assert len(waveform.shape) == 1, "waveform should be 1D"
lengths = np.array([[len(waveform)]], dtype=np.int32)
if padding_duration:
# padding to nearset 10 seconds
samples = np.zeros(
(
1,
padding_duration
* sample_rate
* ((int(duration) // padding_duration) + 1),
),
dtype=np.float32,
)
samples[0, : len(waveform)] = waveform
else:
samples = waveform
samples = samples.reshape(1, -1).astype(np.float32)
data = {
"inputs":[
{
"name": "reference_wav",
"shape": samples.shape,
"datatype": "FP32",
"data": samples.tolist()
},
{
"name": "reference_wav_len",
"shape": lengths.shape,
"datatype": "INT32",
"data": lengths.tolist(),
},
{
"name": "reference_text",
"shape": [1, 1],
"datatype": "BYTES",
"data": [reference_text]
},
{
"name": "target_text",
"shape": [1, 1],
"datatype": "BYTES",
"data": [target_text]
}
]
}
return data
if __name__ == "__main__":
args = get_args()
server_url = args.server_url
if not server_url.startswith(("http://", "https://")):
server_url = f"http://{server_url}"
url = f"{server_url}/v2/models/{args.model_name}/infer"
waveform, sr = sf.read(args.reference_audio)
assert sr == 16000, "sample rate hardcoded in server"
samples = np.array(waveform, dtype=np.float32)
data = prepare_request(samples, args.reference_text, args.target_text)
rsp = requests.post(
url,
headers={"Content-Type": "application/json"},
json=data,
verify=False,
params={"request_id": '0'}
)
result = rsp.json()
audio = result["outputs"][0]["data"]
audio = np.array(audio, dtype=np.float32)
sf.write(args.output_audio, audio, 16000, "PCM_16") |