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# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# 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 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="../../infer/examples/basic/basic_ref_en.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="Some call me nature, others call me mother nature.",
help="",
)
parser.add_argument(
"--target-text",
type=str,
default="I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring.",
help="",
)
parser.add_argument(
"--model-name",
type=str,
default="f5_tts",
choices=["f5_tts", "spark_tts"],
help="triton model_repo module name to request",
)
parser.add_argument(
"--output-audio",
type=str,
default="output.wav",
help="Path to save the output audio",
)
return parser.parse_args()
def prepare_request(
samples,
reference_text,
target_text,
sample_rate=16000,
audio_save_dir: str = "./",
):
assert len(samples.shape) == 1, "samples should be 1D"
lengths = np.array([[len(samples)]], dtype=np.int32)
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
def load_audio(wav_path, target_sample_rate=16000):
assert target_sample_rate == 16000, "hard coding in server"
if isinstance(wav_path, dict):
samples = wav_path["array"]
sample_rate = wav_path["sampling_rate"]
else:
samples, sample_rate = sf.read(wav_path)
if sample_rate != target_sample_rate:
from scipy.signal import resample
num_samples = int(len(samples) * (target_sample_rate / sample_rate))
samples = resample(samples, num_samples)
return samples, target_sample_rate
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"
samples, sr = load_audio(args.reference_audio)
assert sr == 16000, "sample rate hardcoded in server"
samples = np.array(samples, 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, 24000, "PCM_16")
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