37 KiB
Speech Recognition Library Reference
Microphone(device_index: Union[int, None] = None, sample_rate: int = 16000, chunk_size: int = 1024) -> Microphone
Creates a new Microphone instance, which represents a physical microphone on the computer. Subclass of AudioSource.
This will throw an AttributeError if you don't have PyAudio 0.2.11 or later installed.
If device_index is unspecified or None, the default microphone is used as the audio source. Otherwise, device_index should be the index of the device to use for audio input.
A device index is an integer between 0 and pyaudio.get_device_count() - 1 (assume we have used import pyaudio beforehand) inclusive. It represents an audio device such as a microphone or speaker. See the PyAudio documentation for more details.
The microphone audio is recorded in chunks of chunk_size samples, at a rate of sample_rate samples per second (Hertz).
Higher sample_rate values result in better audio quality, but also more bandwidth (and therefore, slower recognition). Additionally, some machines, such as some Raspberry Pi models, can't keep up if this value is too high.
Higher chunk_size values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.
Instances of this class are context managers, and are designed to be used with with statements:
with Microphone() as source: # open the microphone and start recording
pass # do things here - ``source`` is the Microphone instance created above
# the microphone is automatically released at this pointMicrophone.list_microphone_names() -> List[str]
Returns a list of the names of all available microphones. For microphones where the name can't be retrieved, the list entry contains None instead.
The index of each microphone's name in the returned list is the same as its device index when creating a Microphone instance - if you want to use the microphone at index 3 in the returned list, use Microphone(device_index=3).
To create a Microphone instance by name:
m = None
for i, microphone_name in enumerate(Microphone.list_microphone_names()):
if microphone_name == "HDA Intel HDMI: 0 (hw:0,3)":
m = Microphone(device_index=i)Microphone.list_working_microphones() -> Dict[int, str]
Returns a dictionary mapping device indices to microphone names, for microphones that are currently hearing sounds. When using this function, ensure that your microphone is unmuted and make some noise at it to ensure it will be detected as working.
Each key in the returned dictionary can be passed to the Microphone constructor to use that microphone. For example, if the return value is {3: "HDA Intel PCH: ALC3232 Analog (hw:1,0)"}, you can do Microphone(device_index=3) to use that microphone.
To create a Microphone instance for the first working microphone:
for device_index in Microphone.list_working_microphones():
m = Microphone(device_index=device_index)
break
else:
print("No working microphones found!")AudioFile(filename_or_fileobject: Union[str, io.IOBase]) -> AudioFile
Creates a new AudioFile instance given a WAV/AIFF/FLAC audio file filename_or_fileobject. Subclass of AudioSource.
If filename_or_fileobject is a string, then it is interpreted as a path to an audio file on the filesystem. Otherwise, filename_or_fileobject should be a file-like object such as io.BytesIO or similar.
Note that functions that read from the audio (such as recognizer_instance.record or recognizer_instance.listen) will move ahead in the stream. For example, if you execute recognizer_instance.record(audiofile_instance, duration=10) twice, the first time it will return the first 10 seconds of audio, and the second time it will return the 10 seconds of audio right after that. This is always reset when entering the context with a context manager.
WAV files must be in PCM/LPCM format; WAVE_FORMAT_EXTENSIBLE and compressed WAV are not supported and may result in undefined behaviour.
Both AIFF and AIFF-C (compressed AIFF) formats are supported.
FLAC files must be in native FLAC format; OGG-FLAC is not supported and may result in undefined behaviour.
Instances of this class are context managers, and are designed to be used with with statements:
import speech_recognition as sr
with sr.AudioFile("SOME_AUDIO_FILE") as source: # open the audio file for reading
pass # do things here - ``source`` is the AudioFile instance created aboveaudiofile_instance.DURATION # type: float
Represents the length of the audio stored in the audio file in seconds. This property is only available when inside a context - essentially, that means it should only be accessed inside the body of a with audiofile_instance ... statement. Outside of contexts, this property is None.
This is useful when combined with the offset parameter of recognizer_instance.record, since when together it is possible to perform speech recognition in chunks.
However, note that recognizing speech in multiple chunks is not the same as recognizing the whole thing at once. If spoken words appear on the boundaries that we split the audio into chunks on, each chunk only gets part of the word, which may result in inaccurate results.
Recognizer() -> Recognizer
Creates a new Recognizer instance, which represents a collection of speech recognition settings and functionality.
recognizer_instance.energy_threshold = 300 # type: float
Represents the energy level threshold for sounds. Values below this threshold are considered silence, and values above this threshold are considered speech. Can be changed.
This is adjusted automatically if dynamic thresholds are enabled (see recognizer_instance.dynamic_energy_threshold). A good starting value will generally allow the automatic adjustment to reach a good value faster.
This threshold is associated with the perceived loudness of the sound, but it is a nonlinear relationship. The actual energy threshold you will need depends on your microphone sensitivity or audio data. Typical values for a silent room are 0 to 100, and typical values for speaking are between 150 and 3500. Ambient (non-speaking) noise has a significant impact on what values will work best.
If you're having trouble with the recognizer trying to recognize words even when you're not speaking, try tweaking this to a higher value. If you're having trouble with the recognizer not recognizing your words when you are speaking, try tweaking this to a lower value. For example, a sensitive microphone or microphones in louder rooms might have a ambient energy level of up to 4000:
import speech_recognition as sr
r = sr.Recognizer()
r.energy_threshold = 4000
# rest of your code goes hereThe dynamic energy threshold setting can mitigate this by increasing or decreasing this automatically to account for ambient noise. However, this takes time to adjust, so it is still possible to get the false positive detections before the threshold settles into a good value.
To avoid this, use recognizer_instance.adjust_for_ambient_noise(source, duration = 1) to calibrate the level to a good value. Alternatively, simply set this property to a high value initially (4000 works well), so the threshold is always above ambient noise levels: over time, it will be automatically decreased to account for ambient noise levels.
recognizer_instance.dynamic_energy_threshold = True # type: bool
Represents whether the energy level threshold (see recognizer_instance.energy_threshold) for sounds should be automatically adjusted based on the currently ambient noise level while listening. Can be changed.
Recommended for situations where the ambient noise level is unpredictable, which seems to be the majority of use cases. If the ambient noise level is strictly controlled, better results might be achieved by setting this to False to turn it off.
recognizer_instance.dynamic_energy_adjustment_damping = 0.15 # type: float
If the dynamic energy threshold setting is enabled (see recognizer_instance.dynamic_energy_threshold), represents approximately the fraction of the current energy threshold that is retained after one second of dynamic threshold adjustment. Can be changed (not recommended).
Lower values allow for faster adjustment, but also make it more likely to miss certain phrases (especially those with slowly changing volume). This value should be between 0 and 1. As this value approaches 1, dynamic adjustment has less of an effect over time. When this value is 1, dynamic adjustment has no effect.
recognizer_instance.dynamic_energy_adjustment_ratio = 1.5 # type: float
If the dynamic energy threshold setting is enabled (see recognizer_instance.dynamic_energy_threshold), represents the minimum factor by which speech is louder than ambient noise. Can be changed (not recommended).
For example, the default value of 1.5 means that speech is at least 1.5 times louder than ambient noise. Smaller values result in more false positives (but fewer false negatives) when ambient noise is loud compared to speech.
recognizer_instance.pause_threshold = 0.8 # type: float
Represents the minimum length of silence (in seconds) that will register as the end of a phrase. Can be changed.
Smaller values result in the recognition completing more quickly, but might result in slower speakers being cut off.
recognizer_instance.operation_timeout = None # type: Union[float, None]
Represents the timeout (in seconds) for internal operations, such as API requests. Can be changed.
Setting this to a reasonable value ensures that these operations will never block indefinitely, though good values depend on your network speed and the expected length of the audio to recognize.
recognizer_instance.record(source: AudioSource, duration: Union[float, None] = None, offset: Union[float, None] = None) -> AudioData
Records up to duration seconds of audio from source (an AudioSource instance) starting at offset (or at the beginning if not specified) into an AudioData instance, which it returns.
If duration is not specified, then it will record until there is no more audio input.
recognizer_instance.adjust_for_ambient_noise(source: AudioSource, duration: float = 1) -> None
Adjusts the energy threshold dynamically using audio from source (an AudioSource instance) to account for ambient noise.
Intended to calibrate the energy threshold with the ambient energy level. Should be used on periods of audio without speech - will stop early if any speech is detected.
The duration parameter is the maximum number of seconds that it will dynamically adjust the threshold for before returning. This value should be at least 0.5 in order to get a representative sample of the ambient noise.
recognizer_instance.listen(source: AudioSource, timeout: Union[float, None] = None, phrase_time_limit: Union[float, None] = None, snowboy_configuration: Union[Tuple[str, Iterable[str]], None] = None) -> AudioData
Records a single phrase from source (an AudioSource instance) into an AudioData instance, which it returns.
This is done by waiting until the audio has an energy above recognizer_instance.energy_threshold (the user has started speaking), and then recording until it encounters recognizer_instance.pause_threshold seconds of non-speaking or there is no more audio input. The ending silence is not included.
The timeout parameter is the maximum number of seconds that this will wait for a phrase to start before giving up and throwing an speech_recognition.WaitTimeoutError exception. If timeout is None, there will be no wait timeout.
The phrase_time_limit parameter is the maximum number of seconds that this will allow a phrase to continue before stopping and returning the part of the phrase processed before the time limit was reached. The resulting audio will be the phrase cut off at the time limit. If phrase_timeout is None, there will be no phrase time limit.
The snowboy_configuration parameter allows integration with Snowboy, an offline, high-accuracy, power-efficient hotword recognition engine. When used, this function will pause until Snowboy detects a hotword, after which it will unpause. This parameter should either be None to turn off Snowboy support, or a tuple of the form (SNOWBOY_LOCATION, LIST_OF_HOT_WORD_FILES), where SNOWBOY_LOCATION is the path to the Snowboy root directory, and LIST_OF_HOT_WORD_FILES is a list of paths to Snowboy hotword configuration files (*.pmdl or *.umdl format).
This operation will always complete within timeout + phrase_timeout seconds if both are numbers, either by returning the audio data, or by raising a speech_recognition.WaitTimeoutError exception.
recognizer_instance.listen_in_background(source: AudioSource, callback: Callable[[Recognizer, AudioData], Any]) -> Callable[bool, None]
Spawns a thread to repeatedly record phrases from source (an AudioSource instance) into an AudioData instance and call callback with that AudioData instance as soon as each phrase are detected.
Returns a function object that, when called, requests that the background listener thread stop. The background thread is a daemon and will not stop the program from exiting if there are no other non-daemon threads. The function accepts one parameter, wait_for_stop: if truthy, the function will wait for the background listener to stop before returning, otherwise it will return immediately and the background listener thread might still be running for a second or two afterwards. Additionally, if you are using a truthy value for wait_for_stop, you must call the function from the same thread you originally called listen_in_background from.
Phrase recognition uses the exact same mechanism as recognizer_instance.listen(source). The phrase_time_limit parameter works in the same way as the phrase_time_limit parameter for recognizer_instance.listen(source), as well.
The callback parameter is a function that should accept two parameters - the recognizer_instance, and an AudioData instance representing the captured audio. Note that callback function will be called from a non-main thread.
recognizer_instance.recognize_sphinx(audio_data: AudioData, language: str = "en-US", keyword_entries: Union[Iterable[Tuple[str, float]], None] = None, grammar: Union[str, None] = None, show_all: bool = False) -> Union[str, pocketsphinx.pocketsphinx.Decoder]
Performs speech recognition on audio_data (an AudioData instance), using CMU Sphinx.
The recognition language is determined by language, an RFC5646 language tag like "en-US" or "en-GB", defaulting to US English. Out of the box, only en-US is supported. See Notes on using `PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>__ for information about installing other languages. This document is also included under reference/pocketsphinx.rst. The language parameter can also be a tuple of filesystem paths, of the form (acoustic_parameters_directory, language_model_file, phoneme_dictionary_file) - this allows you to load arbitrary Sphinx models.
If specified, the keywords to search for are determined by keyword_entries, an iterable of tuples of the form (keyword, sensitivity), where keyword is a phrase, and sensitivity is how sensitive to this phrase the recognizer should be, on a scale of 0 (very insensitive, more false negatives) to 1 (very sensitive, more false positives) inclusive. If not specified or None, no keywords are used and Sphinx will simply transcribe whatever words it recognizes. Specifying keyword_entries is more accurate than just looking for those same keywords in non-keyword-based transcriptions, because Sphinx knows specifically what sounds to look for.
Sphinx can also handle FSG or JSGF grammars. The parameter grammar expects a path to the grammar file. Note that if a JSGF grammar is passed, an FSG grammar will be created at the same location to speed up execution in the next run. If keyword_entries are passed, content of grammar will be ignored.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the Sphinx pocketsphinx.pocketsphinx.Decoder object resulting from the recognition.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if there are any issues with the Sphinx installation.
recognizer_instance.recognize_google(audio_data: AudioData, key: Union[str, None] = None, language: str = "en-US", , pfilter: Union[0, 1], show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the Google Speech Recognition API.
The Google Speech Recognition API key is specified by key. If not specified, it uses a generic key that works out of the box. This should generally be used for personal or testing purposes only, as it may be revoked by Google at any time.
To obtain your own API key, simply follow the steps on the API Keys page at the Chromium Developers site. In the Google Developers Console, Google Speech Recognition is listed as "Speech API". Note that the API quota for your own keys is 50 requests per day, and there is currently no way to raise this limit.
The recognition language is determined by language, an IETF language tag like "en-US" or "en-GB", defaulting to US English. A list of supported language tags can be found here. Basically, language codes can be just the language (en), or a language with a dialect (en-US).
The profanity filter level can be adjusted with pfilter: 0 - No filter, 1 - Only shows the first character and replaces the rest with asterisks. The default is level 0.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
recognizer_instance.recognize_google_cloud(audio_data: AudioData, credentials_json: Union[str, None] = None, language: str = "en-US", preferred_phrases: Union[Iterable[str], None] = None, show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the Google Cloud Speech API.
This function requires a Google Cloud Platform account; see the Google Cloud Speech API Quickstart for details and instructions. Basically, create a project, enable billing for the project, enable the Google Cloud Speech API for the project, and set up Service Account Key credentials for the project. The result is a JSON file containing the API credentials. The text content of this JSON file is specified by credentials_json. If not specified, the library will try to automatically find the default API credentials JSON file.
The recognition language is determined by language, which is a BCP-47 language tag like "en-US" (US English). A list of supported language tags can be found in the Google Cloud Speech API documentation.
If preferred_phrases is an iterable of phrase strings, those given phrases will be more likely to be recognized over similar-sounding alternatives. This is useful for things like keyword/command recognition or adding new phrases that aren't in Google's vocabulary. Note that the API imposes certain restrictions on the list of phrase strings.
Returns the most likely transcription if show_all is False (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the credentials aren't valid, or if there is no Internet connection.
recognizer_instance.recognize_wit(audio_data: AudioData, key: str, show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the Wit.ai API.
The Wit.ai API key is specified by key. Unfortunately, these are not available without signing up for an account and creating an app. You will need to add at least one intent to the app before you can see the API key, though the actual intent settings don't matter.
To get the API key for a Wit.ai app, go to the app's overview page, go to the section titled "Make an API request", and look for something along the lines of Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX; XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX is the API key. Wit.ai API keys are 32-character uppercase alphanumeric strings.
The recognition language is configured in the Wit.ai app settings.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
recognizer_instance.recognize_bing(audio_data: AudioData, key: str, language: str = "en-US", show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the Microsoft Bing Speech API.
The Microsoft Bing Speech API key is specified by key. Unfortunately, these are not available without signing up for an account with Microsoft Azure.
To get the API key, go to the Microsoft Azure Portal Resources page, go to "All Resources" > "Add" > "See All" > Search "Bing Speech API > "Create", and fill in the form to make a "Bing Speech API" resource. On the resulting page (which is also accessible from the "All Resources" page in the Azure Portal), go to the "Show Access Keys" page, which will have two API keys, either of which can be used for the key parameter. Microsoft Bing Speech API keys are 32-character lowercase hexadecimal strings.
The recognition language is determined by language, a BCP-47 language tag like "en-US" (US English) or "fr-FR" (International French), defaulting to US English. A list of supported language values can be found in the API documentation under "Interactive and dictation mode".
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
recognizer_instance.recognize_houndify(audio_data: AudioData, client_id: str, client_key: str, show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the Houndify API.
The Houndify client ID and client key are specified by client_id and client_key, respectively. Unfortunately, these are not available without signing up for an account. Once logged into the dashboard, you will want to select "Register a new client", and fill in the form as necessary. When at the "Enable Domains" page, enable the "Speech To Text Only" domain, and then select "Save & Continue".
To get the client ID and client key for a Houndify client, go to the dashboard and select the client's "View Details" link. On the resulting page, the client ID and client key will be visible. Client IDs and client keys are both Base64-encoded strings.
Currently, only English is supported as a recognition language.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
recognizer_instance.recognize_ibm(audio_data: AudioData, username: str, password: str, language: str = "en-US", show_all: bool = False) -> Union[str, Dict[str, Any]]
Performs speech recognition on audio_data (an AudioData instance), using the IBM Speech to Text API.
The IBM Speech to Text username and password are specified by username and password, respectively. Unfortunately, these are not available without signing up for an account. Once logged into the Bluemix console, follow the instructions for creating an IBM Watson service instance, where the Watson service is "Speech To Text". IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX, while passwords are mixed-case alphanumeric strings.
The recognition language is determined by language, an RFC5646 language tag with a dialect like "en-US" (US English) or "zh-CN" (Mandarin Chinese), defaulting to US English. The supported language values are listed under the model parameter of the audio recognition API documentation, in the form LANGUAGE_BroadbandModel, where LANGUAGE is the language value.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
recognizer_instance.recognize_whisper(audio_data: AudioData, model: str="base", show_dict: bool=False, load_options: Dict[Any, Any]=None, language:Optional[str]=None, translate:bool=False, **transcribe_options):
Performs speech recognition on audio_data (an AudioData instance), using Whisper.
The recognition language is determined by language, an uncapitalized full language name like "english" or "chinese". See the full language list at https://github.com/openai/whisper/blob/main/whisper/tokenizer.py
model can be any of tiny, base, small, medium, large, tiny.en, base.en, small.en, medium.en. See https://github.com/openai/whisper for more details.
If show_dict is true, returns the full dict response from Whisper, including the detected language. Otherwise returns only the transcription.
You can translate the result to english with Whisper by passing translate=True
Other values are passed directly to whisper. See https://github.com/openai/whisper/blob/main/whisper/transcribe.py for all options
recognizer_instance.recognize_whisper_api(audio_data: AudioData, model: str = "whisper-1", api_key: str | None = None)
Performs speech recognition on audio_data (an AudioData instance), using the OpenAI Whisper API.
This function requires an OpenAI account; visit https://platform.openai.com/signup, then generate API Key in User settings.
Detail: https://platform.openai.com/docs/guides/speech-to-text
Raises a speech_recognition.exceptions.SetupError exception if there are any issues with the openai installation, or the environment variable is missing.
AudioSource
Base class representing audio sources. Do not instantiate.
Instances of subclasses of this class, such as Microphone and AudioFile, can be passed to things like recognizer_instance.record and recognizer_instance.listen. Those instances act like context managers, and are designed to be used with with statements.
For more information, see the documentation for the individual subclasses.
AudioData(frame_data: bytes, sample_rate: int, sample_width: int) -> AudioData
Creates a new AudioData instance, which represents mono audio data.
The raw audio data is specified by frame_data, which is a sequence of bytes representing audio samples. This is the frame data structure used by the PCM WAV format.
The width of each sample, in bytes, is specified by sample_width. Each group of sample_width bytes represents a single audio sample.
The audio data is assumed to have a sample rate of sample_rate samples per second (Hertz).
Usually, instances of this class are obtained from recognizer_instance.record or recognizer_instance.listen, or in the callback for recognizer_instance.listen_in_background, rather than instantiating them directly.
audiodata_instance.get_segment(start_ms: Union[float, None] = None, end_ms: Union[float, None] = None) -> AudioData
Returns a new AudioData instance, trimmed to a given time interval. In other words, an AudioData instance with the same audio data except starting at start_ms milliseconds in and ending end_ms milliseconds in.
If not specified, start_ms defaults to the beginning of the audio, and end_ms defaults to the end.
audiodata_instance.get_raw_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes
Returns a byte string representing the raw frame data for the audio represented by the AudioData instance.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid RAW/PCM audio file.
audiodata_instance.get_wav_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes
Returns a byte string representing the contents of a WAV file containing the audio represented by the AudioData instance.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
Writing these bytes directly to a file results in a valid WAV file.
audiodata_instance.get_aiff_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes
Returns a byte string representing the contents of an AIFF-C file containing the audio represented by the AudioData instance.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
Writing these bytes directly to a file results in a valid AIFF-C file.
audiodata_instance.get_flac_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes
Returns a byte string representing the contents of a FLAC file containing the audio represented by the AudioData instance.
Note that 32-bit FLAC is not supported. If the audio data is 32-bit and convert_width is not specified, then the resulting FLAC will be a 24-bit FLAC.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid FLAC file.