Tagalog text to speech and Taglish speech to text for Philippine teams

Tagalog text to speech in 2026: who offers it and how they differ

Tool menus label the language in different ways, which confuses many first-time users. Filipino is the national language of the Philippines and is based on Tagalog, and most vendors list it as Filipino with the code fil-PH. For a TTS menu the two names mean the same thing, so pick whichever the tool offers.

Most large speech platforms now read it. Google Cloud Text-to-Speech lists ten fil-PH voices across its Standard, WaveNet and Neural2 ranges (Google Cloud). Microsoft Azure lists two, Blessica (female) and Angelo (male), plus higher-quality versions of both (Azure). ElevenLabs lists Filipino for its current multilingual models, and video editors such as CapCut have built-in voices that many creators use for short social clips.

So the choice is rarely about whether a tool speaks Tagalog. It is about price per character, licence terms, how a voice handles Taglish, and whether you can get a Filipino accent. Reddit questions keep returning to those points. A vlogger on r/PinoyVloggers asked for a natural Tagalog voice because "mahal kasi eleven labs" (ElevenLabs is expensive). A small marketing agency with new Philippine accounts asked on r/speechtech whether cloned Tagalog voices were reliable enough for TV and video ads.

A separate service makes sense over an editor's built-in voices when you need the same voice across many videos, a cloned voice, an API for automation, or audio files for radio and ads. Sonex Labs sells one such service, Pāṇini TTS, so read the comparisons here with that in mind. Pāṇini reads Filipino and Taglish text in one voice, but none of its platform voices was recorded by a Filipino speaker. The samples in this guide are read by Liz, an American English voice, so they have her accent.

Writing Tagalog and Taglish scripts that read naturally

Write the script the way your audience talks, not the way a textbook writes. Spoken Filipino is full of po and opo, the particles of politeness, along with Spanish-origin words for time and money and English words that take Tagalog affixes, such as na-check and i-process. A script cleaned into formal Tagalog sounds stiff, and one translated word by word from English sounds foreign.

Prices and numbers deserve the most care, because Filipinos often say them in English inside a Tagalog sentence. "₱1,499" written in digits leaves the engine to choose a language for the number. Written as "one thousand four ninety-nine", it will be said the way a sales host on a livestream would say it. The pair below shows the same line both ways, so you can hear which you prefer.

Keep hyphenated Taglish forms intact. If a copywriter "corrects" na-receive to nareceive, the engine has to guess that an English verb is hiding in it. The same principle applies to Hindi-English mixing, as our guide to Hinglish text to speech explains, and more samples are on the Filipino text to speech page.

What Tagalog voiceover costs per character, in pesos

Characters are the fairest unit for comparing text-to-speech prices, because every service meters them, even when it sells them as monthly credits. A typical month for a small channel or agency account might be 30,000 characters of scripts, and the table converts that month at each listed price.

ElevenLabs sells plans, not a flat rate. Its Starter plan is $6 a month for 30,000 credits, and on its multilingual models one character costs one credit; its Creator plan is $22 for 121,000 credits (ElevenLabs pricing). Its faster models use half a credit or one credit per character, and its plans include tools beyond the voice itself, so a plan can be worth more than the characters alone.

Pāṇini TTS lists $0.03125 per 1,000 characters, the same rate for every language, and the first 10,000 characters and one voice clone are free, with no card; current rates are on the pricing page. Prices in the table are converted at 63.65 Philippine pesos per US dollar (open.er-api.com, 24 October 2026) and were checked on each vendor's pricing page that day.

Voice cloning, commercial licences and "Tagalog with emotion"

Three questions decide whether a Tagalog voice is usable for paid work: whose accent it has, whether you have permission to use the voice, and whether the licence covers commercial use. Each has a clear answer, though not always a convenient one.

Taglish speech to text: no one publishes an error rate

Word error rate (WER) is the share of words a transcriber gets wrong, and no speech-to-text vendor we checked publishes one for Taglish. The gap matters to the Philippine IT-BPM (information technology and business process management) industry, which the industry association IBPAP says earned more than $40 billion in 2025 with 1.9 million workers (Philstar, January 2026). Quality assurance, analytics and voice-bot projects in those contact centres all depend on Taglish transcripts.

What vendors do publish is thin. AssemblyAI places Tagalog in its "good accuracy" tier, meaning 10 to 25 percent WER, without a Taglish figure (AssemblyAI). Soniox's Tagalog page says it handles speech "even when Tagalog is mixed with English" and that accuracy depends on your audio, but gives no Tagalog number (Soniox). The one WER figure on a Soniox Tagalog comparison page, 1.25 percent, comes from an English benchmark, so it says nothing about Taglish.

A transcription blog that reviewed the market in May 2026 says Taglish-specific benchmarks are not published at all, and that errors cluster where English verbs take Tagalog affixes, as in "I-submit mo na" (convertaudiototext). The speech vendor Gladia makes a similar point from the other side, advising buyers to ask for WER on code-switched test sets rather than aggregate multilingual figures (Gladia).

The reason nobody publishes a figure is partly practical. A fair Taglish test set needs real calls, consent to use them and careful human transcripts with agreed spelling rules, and nobody has released one. It is also why vendor demos mislead: short, clean clips in one language are the easy case, and agents who switch language mid-sentence over a noisy headset are the real one.

Valmiki STT, the Sonex speech-to-text API, includes Tagalog and detects the spoken language automatically, including speech that switches mid-sentence. It costs $0.00525 per minute, billed by the second, about ₱0.33 a minute or ₱20.05 an hour of audio. Soniox lists $0.10 to $0.12 per hour, which is cheaper, so price is not the reason to pick Valmiki. No Taglish WER has been published for it either, which is why the next section shows how to measure one yourself.

How to measure Taglish WER on your own calls

The only Taglish accuracy figure worth trusting is one you measure on your own calls. Take 20 or more recordings that represent your traffic, with consent, covering different agents, accounts and noise levels. The NPC has advised that call recording should be proportionate and announced, for example with an automatic voice prompt (Advisory Opinion 2023-010).

Have a person transcribe each call word for word, and agree spelling rules first: 'yung or yung, na-check or nacheck, numbers as digits or words. Without rules, you end up measuring disagreements between transcribers rather than errors. Then run each candidate service on the same files with the same settings, normalise case and punctuation in both texts, and compare.

The script below sends each recording to Valmiki STT and computes the overall WER with the open-source jiwer library. Swap the transcribe function to test any other vendor on the same files. Count names, amounts and account numbers separately as well, since one wrong digit can matter more than ten wrong filler words. Our guide to testing a multilingual voice agent covers the same idea for a full agent.

# pip install requests jiwer
import pathlib
import re

import jiwer
import requests

API_KEY = "YOUR_API_KEY"
URL = "https://api.sonexlabs.com/v1/transcribe"


def transcribe(wav_path):
    with open(wav_path, "rb") as f:
        r = requests.post(
            URL,
            headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "audio/wav"},
            data=f,
            timeout=300,
        )
    r.raise_for_status()
    return r.json()["text"]


def normalise(text):
    text = text.lower().replace("\u2019", "'")
    text = re.sub(r"[^\w\s'-]", " ", text)  # keeps na-check and 'yung intact
    return " ".join(text.split())


refs, hyps = [], []
for wav in sorted(pathlib.Path("calls").glob("*.wav")):
    refs.append(normalise(wav.with_suffix(".txt").read_text(encoding="utf-8")))
    hyps.append(normalise(transcribe(wav)))

print(f"WER over {len(refs)} calls: {jiwer.wer(refs, hyps):.1%}")