AI Clone a person's voice with Piper TTS (Must provide a GPU Server) Deadline: 12 hours max

@Adibs777

Hi, I can handle this voice-cloning project using the workflow you specified: Demucs for removing the music bed, a carefully selected subset of your cleanest recordings for training, and Piper for building the custom voice model. I understand that the priority is faithful reproduction of the speaker’s timbre, pacing, and articulation, not simply creating a voice that sounds vaguely similar. I’ll work with the minimum number of files needed for strong training data, validate the model with test generations, and provide the final compiled ONNX model files with metadata. You’ll also receive sample output before the project is completed so you can confirm the voice quality meets your expectations. I can work within your $35 maximum budget and am ready to review the sample MP3 and begin.

@extreamcode

Hello there, Your real challenge is not merely training a voice, it is producing a clean, broadcast-usable Piper model from music-bed recordings within a 12-hour window. I can use Demucs to separate the dry voice, select the strongest under-30 clips, and apply careful preprocessing before training and validating the speaker’s timbre, pacing, and articulation. I’ll use Bootstrap where appropriate for a compact evaluation workflow, with focused Coding and Software Development to package the final ONNX files and metadata. My Artificial Intelligence experience supports practical model validation, including sample outputs for your approval before delivery. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. I can begin by reviewing the sample and confirming the available GPU environment, then deliver the compiled model and test audio within the agreed 12-hour limit. Which GPU/server environment and Piper version should the ONNX model target, and what broadcast loudness target should guide validation? Which GPU/server environment and Piper version should the ONNX model target? Also, what sample script and broadcast loudness target should I use for final validation? Looking forward to your reply so we can finalize the exact plan. Best regards, Asad

@adeelpirzada

Hi, I am Adeel. I have built AI voice systems before, including MyHeartLine, a voice agent platform handling natural speech generation and transcription at scale, so I am comfortable with the pipeline from audio cleanup to model training. For this I would run demucs first to strip the music bed, pick around 20 to 30 of the cleanest samples, then fine tune from the Piper ryan checkpoint and iterate until pacing and timbre match on playback. I have GPU access ready to go and can start immediately given the tight window. Can you send the sample file now so I can confirm audio quality before committing to the 12 hour timeline?

@faithk149

Training Piper on under thirty clean samples only works if the samples chosen actually cover a real range of the speaker's delivery, since picking clips that all sound the same in pacing and tone produces a model that nails one register and falls apart the moment a script calls for something slightly different, whether calmer, faster, or more emphatic. Demucs handles the music bed separation first, then samples get selected specifically for variety in pacing and articulation rather than just audio cleanliness, so the model trains on the full range of how your speaker actually talks, not just his cleanest sentence repeated thirty times. Training starts from the Piper ryan preset as a bootstrap, with checkpoints reviewed against the original voice throughout rather than only at the end, so timbre drift gets caught early instead of discovered in the final output. Final ONNX files with metadata get delivered along with a direct audio comparison against the original, so approval happens with real evidence, not just a description of quality. Given the twelve hour deadline, is the sample file ready to send right now so sample selection and cleanup can start immediately??

@eugabrielmaximo

Hello, Your project is clear: you need a Piper TTS voice clone from cleaned audio, with a GPU available and final ONNX files delivered fast. The main risk here is not training time, it is getting the voice timbre, pacing, and articulation close enough after removing the music bed cleanly. I understand you want to avoid wasting time on all 100 plus files, and only use the strongest samples for a cleaner clone. I can help prepare the training set, clean the background bed with demucs, and focus training on the most usable recordings so the model stays consistent under normal broadcast processing. My approach would be: • select the best samples for AI Training Data quality • clean and normalize the voice before Training • bootstrap from a Piper preset, then refine toward the speaker’s tone • export the final ONNX model with metadata and provide test audio before delivery I have worked on Training workflows where sample quality and consistency matter more than raw volume, especially when the target is a voice that must sound natural on playback. If you want, I can start by reviewing one sample and outlining the exact file set I would use. Best regards

@indreshpnk

Hello, I understand you need an experienced developer to train a custom AI voice model using your permitted speaker recordings, clean the source audio, and produce a Piper-compatible voice model that closely matches the original timbre, pacing, and articulation. The goal is to deliver a tested model and compiled ONNX files ready for your intended use. Here’s what I can provide: Prepare selected recordings by separating/cleaning the voice and reducing background music before training. Set up and fine-tune the Piper TTS training workflow using suitable training data and GPU resources. Validate generated speech against your expectations and provide the final compiled ONNX model files with metadata and sample audio. I bring over 4+ years of experience in Python, software development, audio/data processing, automation, and AI-related workflows, with a focus on reproducible and well-documented implementations. Just to clarify a few things: Can you provide the sample MP3 and confirm the exact Piper version/model setup you want to target? What GPU environment is available for the training, and do you already have the required Demucs/Piper training setup? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@creativebooster

Hey, I understand you need an experienced AI voice cloning and Piper TTS specialist within your $35 budget; I will deliver a reliable, high-quality setup tailored to your requirements. Send me a message with the full details, and I can start immediately.

@venplura

EVEN IF YOU DON'T HIRE ME, YOU'LL WALK AWAY WITH A FREE CONSULTATION. You need a high-quality voice clone trained on Piper TTS that captures your speaker's timbre, pacing, and articulation for live broadcast use. The two critical challenges are cleaning background music from your samples using demucs and training a model that reproduces the original voice faithfully enough for on-air deployment. Here's how I'd approach it: - Process your audio files through demucs to isolate clean vocal tracks from the music bed - Select 20-30 of your best samples and train a custom Piper model bootstrapped from the ryan preset - Deliver fully compiled ONNX files with metadata and sample audio output for your approval before project completion I've worked extensively with AI automation workflows and model training across multiple platforms. Let me walk you through the process with no obligation. Kind regards, Cornelius

@selaaf

Fewer than 30 clean files can be enough for Piper, as long as the music bed comes off well. First I'd run your sample through Demucs and send back the cleaned voice for you to hear. Then I'd fine-tune from the ryan preset and send you audio to approve before the ONNX export. Do you want the voice tuned for a specific speaking style, like calm reading or energetic promos? Send the sample over whenever it suits you. Sela

@Mou192

I can train a high-quality Piper TTS voice model from your supplied recordings, using Demucs for music/voice separation and selecting only the cleanest samples needed for training. I’ll fine-tune the model to preserve your speaker’s timbre, pacing, and articulation, validate the generated speech against the source voice, and provide the final compiled ONNX model with metadata plus sample audio for approval. I’m comfortable working with GPU-based training and Piper workflows.

@ozair94

Hi, Your project doesn't need someone who simply follows the brief. It needs someone who understands what you're trying to achieve and figures out how to get you there. That's how I approach projects. Before writing code or jumping into execution, I want to understand the problem, the goal, and what success actually looks like for your business. I've spent the last several years turning business problems into working digital products, leading developers and designers, managing projects from idea to delivery, and delivering 40+ web and mobile projects. Do I know everything? Absolutely not. But I know how to solve problems. If I don't have the answer, I'll find it. I'll research, learn, test, ask the right questions, and keep working until we have a solution. And once I'm on board: It's our business. Our problem. Our responsibility. I'll give you my honest opinion, challenge ideas when I see a better approach, and never agree just for the sake of keeping things comfortable. I work with clients because I care about building things that succeed. If you're looking for someone who will take ownership rather than simply complete tasks, I'd be happy to learn more about what you're building.

@alfatrade2

Cleaning the bed music out with demucs before training is the right call — stems that still have music bleed are the usual reason a Piper clone comes out muddy or robotic. Do you already have the ~30 cleanest clips picked, or should I go through the 100+ and select those myself based on dryness and clarity? I've built a voice agent that used offline speech-to-text and dialogue handling, so I'm comfortable working through an audio pipeline end to end on a rented GPU box.

@iFixNow

Hi, there With your files, the quality risk is the music bed, not Piper. Demucs removes most of it, but faint music and a "watery" edge stay behind, and Piper learns that as part of the voice, so it airs on every read. Also, Piper moved to the new piper1-gpl project, and older training guides now break at ONNX export, where many clones fail late. As CTO of Assist A Boss, I trained a Piper voice for a Florida radio client's automated weather and traffic reads. I picked 24 shows, ran Demucs, dropped any clip where music still bled through, and kept about 70 minutes of clean speech with checked transcripts. Fine-tuning from ryan medium on my own RTX 4090 server took about 6 hours, and the voice kept the host's pacing and tone so closely that listeners never noticed. It runs faster than real time on a plain CPU and airs 16 hours a day. How I'd deliver yours: - Demucs on the cleanest 25 to 30 files, then clip-by-clip checks - Whisper transcripts, corrected where names or terms are off - Fine-tune from ryan medium on my GPU, testing samples as it trains - Test reads of your own scripts for your approval - Final ONNX with its config file, ready for your server If you’re looking for scalable, practical value rather than just a one-time project, I can help you achieve meaningful results through small but lasting improvements that will exceed your expectations and surprise everyone involved. William Berry

@SonnyDube

I am a perfect fit for your project. I will extract clean voice audio using Demucs, train your custom Piper TTS model bootstrapped from the Ryan preset on my dedicated GPU, and deliver the final compiled ONNX and metadata files within your 12-hour deadline for seamless radio broadcast output. While I am new to Freelancer, I have strong experience and have delivered similar solutions outside the platform. I offer a free consultation to better understand the project and provide the best technical approach. I would love to chat more about your project! Regards, Sonny Dube

@xlogicsolutions

Hello, I will outline an approach to clean audio, bootstrap a Piper-friendly voice model, and export ONNX artifacts. Approach: • Review data licensing and constraints. • Plan a focused subset (≤30 clips), apply denoise, prepare Piper-compatible inputs. • Validate timbre and pacing, iterate as needed. • Export ONNX with metadata and provide samples for approval. I have hands-on experience with audio preprocessing, pipelines, and model exports, and I can adapt to Piper's workflow with handover docs. How do you define acceptable fidelity and latency for live Piper TTS playback, and which Piper version and deployment environment will be used for the final integration? Best regards, Umer Hayat

@ilyaashassiem

The key challenge here is getting clean, consistent speech from the music-backed recordings before fine-tuning Piper. The ONNX export itself is straightforward if the dataset and training setup are correct, but the final similarity will depend heavily on the recoverable voice quality and transcripts. I’d first assess one sample, confirm the usable speech quality and training approach, then fine-tune Piper and provide the ONNX model, metadata and test audio for approval. Could you send the sample MP3 so I can assess whether the voice can be separated cleanly enough for training? Regards, Ilyaas

@farukhkaleem1

I have extensive experience with AI voice cloning and Piper TTS, and I can refine the audio quality while ensuring the voice model closely matches the original. With access to a robust GPU, I can deliver high-quality results well within your timeline. Let's discuss how I can bring your project to life.

@pavlev8

Hi, I’d first use Demucs to isolate the dry vocal from the music bed, then manually select the cleanest clips with consistent tone, minimal bleed, and useful phonetic coverage. Those clips would be segmented, normalized, transcribed, and converted into Piper’s required training dataset format. I’d fine-tune from the Ryan checkpoint only if its speaker characteristics help convergence; otherwise I’d train/fine-tune a cleaner Piper-compatible voice model on the prepared dataset. After validation, I’d compare generated speech against held-out samples for timbre, pacing, articulation, noise, and stability, then export the final ONNX model plus metadata/config files for your server. I already work with audio preprocessing, AI voice pipelines, dataset preparation, model training, ONNX export, and GPU workflows. I can work any time zone what you want. I am ready to start the project right away. Best regards, Pavle

@ankits0192

Hello, I understand you need a focused Piper TTS voice-cloning job completed quickly, using a GPU environment and a carefully selected subset of your recordings rather than processing all 100+ files. I can structure the workflow around cleaning the selected recordings, preparing consistent training data, adapting Piper from a suitable base model, and validating the resulting voice quality. The training process will prioritize speaker timbre, pacing, pronunciation, and articulation while minimizing artifacts introduced by the original music bed. I’ll test different suitable samples and preprocessing choices, then generate sample speech so you can evaluate the result before final delivery. The final handover will include the compiled Piper-compatible ONNX model files with metadata and the required supporting files, ready for your own deployment workflow. Looking forward to work with you. Thanks