SAM

Software Automatic Mouth - Tiny Speech Synthesizer

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Quick Overview

SAM (Software Automatic Mouth) is an open-source speech synthesizer originally developed for 8-bit computers in the 1980s. This GitHub repository contains a modern C port of the original assembly code, allowing SAM to run on contemporary systems. It provides a unique, retro-style voice synthesis experience with a small footprint.

Pros

Lightweight and portable, capable of running on various platforms
Offers a nostalgic, 8-bit era speech synthesis sound
Highly customizable with adjustable speech parameters
Simple to use with a straightforward API

Cons

Limited in terms of natural-sounding speech compared to modern synthesizers
Lacks support for multiple languages or accents
May require additional effort to integrate into modern applications
Documentation could be more comprehensive for advanced usage

Code Examples

Basic usage to generate speech:

#include "sam.h"

int main() {
    SetInput("Hello, world!");
    SetSpeed(72);
    SetPitch(64);
    SetMouth(128);
    SetThroat(128);
    SAMMain();
    return 0;
}

Adjusting speech parameters:

#include "sam.h"

int main() {
    SetInput("This is a test of different speech parameters.");
    SetSpeed(120);  // Faster speech
    SetPitch(80);   // Higher pitch
    SetMouth(110);  // Adjusted mouth shape
    SetThroat(150); // Adjusted throat shape
    SAMMain();
    return 0;
}

Using phonetic input:

#include "sam.h"

int main() {
    EnablePhonemeOutput(1);
    SetInput("/HEH4LOW WERLD");  // Phonetic representation of "Hello World"
    SAMMain();
    return 0;
}

Getting Started

To use SAM in your project:

Clone the repository: git clone https://github.com/s-macke/SAM.git
Navigate to the src directory: cd SAM/src
Compile the source: gcc *.c -o sam
Run SAM: ./sam "Hello, world!"

To integrate SAM into your C project, include the necessary header files and link against the compiled library. Make sure to initialize SAM, set the input text, and call SAMMain() to generate speech output.

Competitor Comparisons

whisper

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Pros of Whisper

Advanced speech recognition capabilities for multiple languages
Backed by OpenAI's extensive research and development resources
Regularly updated with new features and improvements

Cons of Whisper

Requires significant computational resources for optimal performance
More complex setup and usage compared to simpler audio tools
Larger model size and longer processing times for some tasks

Code Comparison

SAM (Software Automatic Mouth):

void Output_Phoneme(unsigned char phoneme)
{
    unsigned char s;
    unsigned char phase1;
    unsigned char phase2;
    unsigned char phase3;
    unsigned char mem38;
}

Whisper:

def transcribe(model, audio, task, language, **decode_options):
    mel = log_mel_spectrogram(audio)
    segment = pad_or_trim(mel, N_FRAMES).to(model.device)
    decode_options["language"] = language
    result = decode(model, segment, **decode_options)
    return result

Key Differences

SAM focuses on speech synthesis, while Whisper specializes in speech recognition
SAM is written in C and assembly, Whisper is primarily Python-based
Whisper offers more advanced features and broader language support
SAM is lighter and simpler, suitable for retro computing and embedded systems

vosk-api

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Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

Pros of vosk-api

Focuses on speech recognition, offering a more specialized solution for audio processing
Supports multiple languages and provides pre-trained models
Designed for real-time speech recognition with low latency

Cons of vosk-api

More complex setup and integration compared to SAM's simplicity
Requires more computational resources for speech recognition tasks
Limited to speech recognition, while SAM offers broader audio synthesis capabilities

Code Comparison

vosk-api (Python):

from vosk import Model, KaldiRecognizer
import pyaudio

model = Model("model")
rec = KaldiRecognizer(model, 16000)

p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=8000)

SAM (C):

#include "sam.h"

int main() {
    SetInput("Hello World");
    SetSpeed(72);
    SetPitch(64);
    SetMouth(128);
    SetThroat(128);
    SAMMain();
    return 0;
}

The code snippets demonstrate the different focus areas of the two projects. vosk-api is centered around speech recognition, while SAM is geared towards speech synthesis. vosk-api requires more setup for audio input and model initialization, whereas SAM offers a simpler interface for generating speech output.

TTS

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🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

Pros of TTS

More comprehensive and feature-rich text-to-speech solution
Supports multiple languages and voice models
Active development with frequent updates and community support

Cons of TTS

More complex setup and usage compared to SAM
Requires more computational resources for training and inference

Code Comparison

TTS example:

from TTS.api import TTS

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC")
tts.tts_to_file(text="Hello world!", file_path="output.wav")

SAM example:

#include "render.h"
#include "sam.h"

SetInput("Hello world!");
sam_flush();

Key Differences

TTS is a modern, Python-based library with support for various TTS models
SAM is a C implementation of a classic 1980s speech synthesizer
TTS offers more natural-sounding speech output
SAM provides a unique retro-style voice with lower resource requirements

Use Cases

TTS: Modern applications requiring high-quality, multi-language speech synthesis
SAM: Retro-style projects, educational purposes, or resource-constrained environments

DeepSpeech

25,032

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

Pros of DeepSpeech

Utilizes modern deep learning techniques for speech recognition
Supports multiple languages and can be fine-tuned for specific domains
Actively maintained with regular updates and improvements

Cons of DeepSpeech

Requires significant computational resources for training and inference
More complex to set up and use compared to simpler text-to-speech systems
May have higher latency for real-time applications

Code Comparison

DeepSpeech (Python):

import deepspeech
model = deepspeech.Model('path/to/model.pbmm')
text = model.stt(audio_buffer)

SAM (C):

void SetMouthThroat(unsigned char mouth, unsigned char throat)
{
    sam->mouth = mouth;
    sam->throat = throat;
}

Key Differences

DeepSpeech focuses on speech recognition (audio to text), while SAM is a text-to-speech system
DeepSpeech uses neural networks, whereas SAM employs older formant synthesis techniques
DeepSpeech is more flexible and accurate for modern applications, but SAM is simpler and requires fewer resources
DeepSpeech is actively maintained, while SAM is a historical project with limited updates

Use Cases

DeepSpeech: Modern speech recognition applications, voice assistants, transcription services
SAM: Retro computing projects, educational purposes, low-resource environments

silero-models

4,841

Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple

Pros of silero-models

Focuses on speech recognition and text-to-speech models
Supports multiple languages and provides pre-trained models
Actively maintained with regular updates and improvements

Cons of silero-models

More complex setup and usage compared to SAM
Requires more computational resources for running models
Limited to speech-related tasks, less versatile than SAM

Code Comparison

SAM:

import sam
sam.run_program("example.sam")

silero-models:

import torch
import soundfile as sf
from silero import silero_stt

model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                       model='silero_stt',
                                       language='en')

Summary

SAM is a simple assembler and machine emulator, while silero-models focuses on speech recognition and synthesis. SAM is easier to use for basic assembly language tasks, while silero-models offers more advanced speech processing capabilities but requires more setup and resources. The choice between them depends on the specific project requirements and the desired level of complexity.

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README

SAM

Software Automatic Mouth - Tiny Speech Synthesizer

What is SAM?

Sam is a very small Text-To-Speech (TTS) program written in C, that runs on most popular platforms. It is an adaption to C of the speech software SAM (Software Automatic Mouth) for the Commodore C64 published in the year 1982 by Don't Ask Software (now SoftVoice, Inc.). It includes a Text-To-Phoneme converter called reciter and a Phoneme-To-Speech routine for the final output. It is so small that it will work also on embedded computers. On my computer it takes less than 39KB (much smaller on embedded devices as the executable-overhead is not necessary) of disk space and is a fully stand alone program. For immediate output it uses the SDL-library, otherwise it can save .wav files.

An online version and executables for Windows can be found on the web site: http://simulationcorner.net/index.php?page=sam

Compile

Simply type "make" in your command prompt. In order to compile without SDL remove the SDL statements from the CFLAGS and LFLAGS variables in the file "Makefile".

It should compile on every UNIX-like operating system. For Windows you need Cygwin or MinGW( + libsdl).

Fork

Take a look at https://github.com/vidarh/SAM for a more refactored and cleaner version of the code.

Usage

type

./sam I am Sam

for the first output.

If you have disabled SDL try

./sam -wav i_am_sam.wav I am Sam

to get a wav file. This file can be played by many media players available for the PC.

you can try other options like -pitch number -speed number -throat number -mouth number

Some typical values written in the original manual are:

DESCRIPTION          SPEED     PITCH     THROAT    MOUTH
Elf                   72        64        110       160
Little Robot          92        60        190       190
Stuffy Guy            82        72        110       105
Little Old Lady       82        32        145       145
Extra-Terrestrial    100        64        150       200
SAM                   72        64        128       128

It can even sing look at the file "sing" for a small example.

For the phoneme input table look in the Wiki.

A description of additional features can be found in the original manual at http://www.retrobits.net/atari/sam.shtml or in the manual of the equivalent Apple II program http://www.apple-iigs.info/newdoc/sam.pdf

Adaption To C

This program (disassembly at http://hitmen.c02.at/html/tools_sam.html) was converted semi-automatic into C by converting each assembler opcode. e. g.

lda 56		=>	A = mem[56];
jmp 38018  	=>	goto pos38018;
inc 38		=>	mem[38]++;
.			.
.			.

Then it was manually rewritten to remove most of the jumps and register variables in the code and rename the variables to proper names. Most of the description below is a result of this rewriting process.

Unfortunately it is still unreadable. But you should see from where I started :)

Short description

First of all I will limit myself here to a very coarse description. There are very many exceptions defined in the source code that I will not explain. Also a lot of code is unknown for me e. g. Code47503. For a complete understanding of the code I need more time and especially more eyes have a look on the code.

Reciter

It changes the english text to phonemes by a ruleset shown in the wiki.

The rule " ANT(I)", "AY", means that if he find an "I" with previous letters " ANT", exchange the I by the phoneme "AY".

There are some special signs in this rules like # & @ ^ + : % which can mean e. g. that there must be a vocal or a consonant or something else.

With the -debug option you will get the corresponding rules and the resulting phonemes.

Output

Here is the full tree of subroutine calls:

SAMMain() Parser1() Parser2() Insert() CopyStress() SetPhonemeLength() Code48619() Code41240() Insert() Code48431() Insert()

Code48547
	Code47574
		Special1
		Code47503
		Code48227

SAMMain() is the entry routine and calls all further routines. Parser1 transforms the phoneme input and transforms it to three tables phonemeindex[] stress[] phonemelength[] (zero at this moment)

This tables are now changed:

Parser2 exchanges some phonemes by others and inserts new. CopyStress adds 1 to the stress under some circumstances SetPhonemeLength sets phoneme lengths. Code48619 changes the phoneme lengths Code41240 adds some additional phonemes Code48431 has some extra rules

The wiki shows all possible phonemes and some flag fields.
The final content of these tables can be seen with the -debug command.

In the function PrepareOutput() these tables are partly copied into the small tables: phonemeindexOutput[] stressOutput[] phonemelengthOutput[] for output.

Final Output

Except of some special phonemes the output is build by a linear combination:

A =   A1 * sin ( f1 * t ) +
      A2 * sin ( f2 * t ) +
      A3 * rect( f3 * t )

where rect is a rectangular function with the same periodicity like sin. It seems really strange, but this is really enough for most types of phonemes.

Therefore the above phonemes are converted with some tables to pitches[] frequency1[] = f1 frequency2[] = f2 frequency3[] = f3 amplitude1[] = A1 amplitude2[] = A2 amplitude3[] = A3

Above formula is calculated in one very good omptimized routine. It only consist of 26 commands:

48087: 	LDX 43		; get phase	
CLC		
LDA 42240,x	; load sine value (high 4 bits)
ORA TabAmpl1,y	; get amplitude (in low 4 bits)
TAX		
LDA 42752,x	; multiplication table
STA 56		; store 

LDX 42		; get phase
LDA 42240,x	; load sine value (high 4 bits)
ORA TabAmpl2,y	; get amplitude (in low 4 bits)
TAX		
LDA 42752,x	; multiplication table
ADC Var56	; add with previous values
STA 56		; and store

LDX 41		; get phase
LDA 42496,x	; load rect value (high 4 bits)
ORA TabAmpl3,y	; get amplitude (in low 4 bits)
TAX		
LDA 42752,x	; multiplication table
ADC 56		; add with previous values

ADC #136		
LSR A		; get highest 4 bits
LSR A		
LSR A		
LSR A		
STA 54296	;SID   main output command

The rest is handled in a special way. At the moment I cannot figure out in which way. But it seems that it uses some noise (e. g. for "s") using a table with random values.

License

The software is a reverse-engineered version of a software published more than 34 years ago by "Don't ask Software".

The company no longer exists. Any attempt to contact the original authors failed. Hence S.A.M. can be best described as Abandonware (http://en.wikipedia.org/wiki/Abandonware)

As long this is the case I cannot put my code under any specific open source software license. However the software might be used under the "Fair Use" act (https://en.wikipedia.org/wiki/FAIR_USE_Act) in the USA.

Contact

If you have questions don' t hesitate to ask me. If you discovered some new knowledge about the code please mail me.

Sebastian Macke Email: sebastian@macke.de

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