Text-to-speech service to transform text into natural-sounding speech using deep learning technologies.
Voice Quality
Murf API Samples
Mean Opinion Score
Fiction
N/A
Non-Fiction
N/A
Conversation
N/A
Amazon Polly Samples
Mean Opinion Score
Fiction
3.00
Non-Fiction
2.51
Conversation
2.63
Mean Opinion Score (MOS) is a numerical measure that represents the perceived quality of audio samples, commonly used in evaluating text-to-speech systems.
The score ranges from 1 to 5, with 1 indicating poor quality and 5 signifying excellent quality.
These scores are derived from comprehensive, professionally-conducted evaluations, and are anonymized to ensure unbiased results.
Features
Murf API Features
Voice Cloning
Multi-lingual
Per-word Timestamps
Pitch Control
Speed Control
Phone Formats (e.g. pcm_mulaw)
Amazon Polly Features
Voice Cloning
Multi-lingual
Per-word Timestamps
Pitch Control
Speed Control
Phone Formats (e.g. pcm_mulaw)
Features - Conclusion
Both Murf API and Amazon Polly offer a range of features that cater to developers looking to integrate text-to-speech capabilities into their applications, with both supporting multi-lingual text, pitch and speed control, and compatibility with phone formats.
However, Murf API distinguishes itself with voice cloning capabilities, a feature not available in Amazon Polly.
On the other hand, Amazon Polly provides per-word timestamps, which could be crucial for applications requiring precise synchronization between text and speech, a feature that Murf API lacks.
Pricing & Plans
Murf API Pricing
API Subscription
$3000/year
12M characters (per year)
Amazon Polly Pricing
Free
$0/mo
1M characters (for first 12 months only)
Pay As You Go
$16per
1M characters
Pricing & Plans - Conclusion
Amazon Polly offers a more cost-effective solution for both initial and long-term text-to-speech needs compared to Murf API.
With a free tier for the first 12 months and a lower cost for additional usage, Amazon Polly is particularly advantageous for users with varying levels of text conversion requirements.
Overall, for those prioritizing budget, Amazon Polly presents a better value proposition over time.
Customer Reviews
Murf API Reviews
3.8 out of 5
Average of 819 ratings from leading review sites.
Murf API generally receives praise for its variety of natural-sounding voices, user-friendly interface, and quick setup, making it a popular choice for voiceover projects. Customers appreciate the diverse voice options and the ability to customize speech patterns. However, there are significant concerns about the pricing model, which many find too expensive and restrictive in terms of voice generation limits. Additionally, there are complaints about customer service and misleading information regarding free trials and subscriptions. Issues with billing practices and refund policies are also frequently mentioned, causing distrust among some users.
Voice quality
User interface
Setup
Voice variety
Customization
Pricing
Customer service
Billing practices
Refund policy
Amazon Polly Reviews
4.4 out of 5
Average of 68 ratings from leading review sites.
Customers appreciate Amazon Polly for its natural-sounding voices, ease of use, and integration with AWS services. They find it beneficial for various applications like IVR systems, content creation, and multilingual support. However, concerns about cost, limited customization options, and occasional unnatural inflections in the voices are common. The service's scalability and fast response times are highlighted as significant advantages, helping businesses efficiently manage large-scale projects.
When comparing Murf API and Amazon Polly for text-to-speech services, both platforms offer unique advantages.
Murf API stands out with its voice cloning feature and a comprehensive subscription plan, making it suitable for users needing advanced voice customization.
However, Amazon Polly presents a more budget-friendly option with its free tier and pay-as-you-go pricing, alongside offering per-word timestamps for precise text-to-speech synchronization, making it an attractive choice for a wide range of applications that require high-quality, cost-effective text-to-speech solutions.
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