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https://github.com/ionite34/h2p-parser
Heteronym to Phoneme Parser
https://github.com/ionite34/h2p-parser
g2p grapheme-to-phoneme natural-language-processing phoneme-conversion python
Last synced: about 2 months ago
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Heteronym to Phoneme Parser
- Host: GitHub
- URL: https://github.com/ionite34/h2p-parser
- Owner: ionite34
- License: apache-2.0
- Created: 2022-04-26T17:09:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-04T18:50:12.000Z (about 1 year ago)
- Last Synced: 2024-10-11T19:45:49.001Z (2 months ago)
- Topics: g2p, grapheme-to-phoneme, natural-language-processing, phoneme-conversion, python
- Language: Python
- Homepage:
- Size: 1.9 MB
- Stars: 14
- Watchers: 1
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Heteronym to Phoneme Parser
[](https://github.com/ionite34/h2p-parser/actions/workflows/python-package.yml)
[](https://github.com/ionite34/h2p-parser/actions/workflows/codeql-analysis.yml)
[](https://codecov.io/gh/ionite34/h2p-parser)
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fionite34%2Fh2p-parser?ref=badge_shield)
### Fast parsing of English Heteronyms to Phonemes using contextual part-of-speech.
Provides the ability to convert heteronym graphemes to their phonetic pronunciations.
Designed to be used in conjunction with other fixed grapheme-to-phoneme dictionaries such as [`CMUdict`](https://github.com/cmusphinx/cmudict)
This package also offers a combined Grapheme-to-Phoneme dictionary,
combining the functionality of fixed lookups handled by CMUdict and context-based parsing as
offered by this module.
## Usage
### 1. Combined Grapheme-to-Phoneme dictionary
The `CMUDictExt` class combines a pipeline for context-based heteronym parsing to phonemes and a fixed dictionary lookup
replacement using the CMU Pronouncing Dictionary.
Example:
```python
from h2p_parser.cmudictext import CMUDictExt
CMUDictExt = CMUDictExt()
# Parsing replacements for a line. This can be one or more sentences.
line = CMUDictExt.convert("The cat read the book. It was a good book to read.")
# -> "{DH AH0} {K AE1 T} {R EH1 D} {DH AH0} {B UH1 K}. {IH1 T} {W AA1 Z} {AH0} {G UH1 D} {B UH1 K} {T UW1} {R IY1 D}."
```
> Additional optional parameters are available when defining a `CMUDictExt` instance:
| Parameter | Type | Default Value | Description |
|--------------------|--------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `cmu_dict_path` | `str` | `None` | Path to a custom CMUDict file in `.txt` format |
| `h2p_dict_path` | `str` | `None` | Path to a custom H2p Dictionary file in `.json` format. See the [example.json](h2p_parser/data/example.json) for the expected format. |
| `cmu_multi_mode` | `int` | `0` | Default selection index for CMUDict entries with multiple pronunciations as donated by the `(1)` or `(n)` format |
| `process_numbers` | `bool` | `True` | Toggles conversion of some numbers and symbols to their spoken pronunciation forms. See [numbers.py](h2p_parser/text/numbers.py) for details on what is covered. |
| `phoneme_brackets` | `bool` | `True` | Surrounds phonetic words with curly brackets i.e. `{R IY1 D}` |
| `unresolved_mode` | `str` | `keep` | Unresolved word resolution modes:
`keep` - Keeps the text-form word in the output.
`remove` - Removes the text-form word from the output.
`drop` - Returns the line as `None` if any word is unresolved. |
### 2. Heteronym-to-Phoneme parsing only
To use only the core heteronym-to-phoneme parsing functions,
without fixed dictionary support, use `H2p` class.
Example:
```python
from h2p_parser.h2p import H2p
h2p = H2p(preload=True) # preload flag improves first-inference performance
# checking if a line contains a heteronym
state = h2p.contains_het("There are no heteronyms in this line.")
# -> False
# replacing a single line
line = h2p.replace_het("I read the book. It was a good book to read.")
# -> "I {R EH1 D} the book. It was a good book to {R IY1 D}."
# replacing a list of lines
lines = h2p.replace_het_list(["I read the book. It was a good book to read.",
"Don't just give the gift; present the present.",
"If you were to reject the product, it would be a reject."])
# -> ["I {R EH1 D} the book. It was a good book to {R IY1 D}.",
# "Don't just give the gift; {P R IY0 Z EH1 N T} the {P R EH1 Z AH0 N T}.",
# "If you were to {R IH0 JH EH1 K T} the product, it would be a {R IY1 JH EH0 K T}."]
```
>**Note**: Depending on your performance requirements, there is a speed improvement for processing large text line batches by using `replace_het_list()` with a list of all text lines, instead of making repeated calls to `replace_het()`. See the performance section for more details and guidelines for optimizations.
## License
The code in this project is released under [Apache License 2.0](LICENSE).
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