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---
library_name: transformers
language:
- en
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- custom
model-index:
- name: 'SpeechT5 TTS technical train2 '
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
| **PAGE** | **LINK** |
|-------------------------------------|------------------------------------------------------------------------------------------------------|
| **MARATHI TTS GITHUB LINK LINK** | [MARATHI TTS REPO](https://github.com/dawarepranav/speechT5_marathi_finetuned-) |
| **HUGGING FACE ENG TECHNICAL DATA** | [HUGGING FACE TECHNICAL DATA ](https://huggingface.co/pranavdaware/speecht5_tts_technical_train2) |
| **HUGGING FACE MARATHI TTS** | [HUGGING FACE MARATHI TTS ](https://huggingface.co/pranavdaware/speecht5_tts_marathi_train2) |
| **REPORT** | [REPORT](https://github.com/dawarepranav/speecht5_tts_english_technical_data/blob/main/A%20Technical%20Report.docx) |
# 🎤 SpeechT5 TTS Technical Train2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) using a custom dataset, specifically trained for *Text-to-Speech (TTS)* tasks.
🎯 *Key Metric:*
- *Loss* on the evaluation set: 0.3763
📢 *Listen to the generated sample:*
The text is " Hello ,few technical terms i used while fine tuning are API and REST and CUDA and TTS."
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/66f64964584cae45b5494560/JYJmDNPHnBRLuvqGTJQSu.wav"></audio>
---
## 📝 Model Description
The *SpeechT5 TTS Technical Train2* is built on the *SpeechT5* architecture and was fine-tuned for speech synthesis (TTS). The fine-tuning focused on improving the naturalness and clarity of the generated audio from text.
🛠 *Base Model*: [Microsoft SpeechT5](https://huggingface.co/microsoft/speecht5_tts)
📚 *Dataset*: Custom (specific details to be provided)
---
## 🔧 Intended Uses & Limitations
### ✅ *Primary Use Cases:*
- *Text-to-Speech (TTS)* for technical Interview Texts .
- *Virtual Assistants*:
### ⚠ *Limitations:*
- Best suited for English TTS tasks.
- Require further fine-tuning on Large dataset .
---
## 📅 Training Data
The model was fine-tuned on a *custom dataset*, curated for enhancing TTS outputs. This dataset consists of various types of text that help the model generate more natural speech, making it suitable for TTS applications.
### ⚙ *Hyperparameters:*
The model was trained with the following hyperparameters:
- *Learning Rate*: 1e-05
- *Train Batch Size*: 16
- *Eval Batch Size*: 8
- *Seed*: 42
- *Gradient Accumulation Steps*: 2
- *Total Train Batch Size*: 32
- *Optimizer*: AdamW (betas=(0.9, 0.999), epsilon=1e-08)
- *LR Scheduler Type*: Linear
- *Warmup Steps*: 50
- *Training Steps*: 500
- *Mixed Precision Training*: Native AMP
### ⚙ *📊 Training Results:*:
| 🏋♂ Training Loss | 🕑 Epoch | 🛤 Step | 📉 Validation Loss |
|:-------------------:|:-------:|:-------:|:-----------------:|
| 1.1921 | 100.0 | 100 | 0.4136 |
| 0.8435 | 200.0 | 200 | 0.3791 |
| 0.8294 | 300.0 | 300 | 0.3766 |
| 0.7959 | 400.0 | 400 | 0.3744 |
| 0.7918 | 500.0 | 500 | 0.3763 |
### 📦 Framework Versions
- *Transformers*: 4.46.0.dev0
- *PyTorch*: 2.4.1+cu121
- *Datasets*: 3.0.2
- *Tokenizers*: 0.20.1 |