# Hebrew Voice Bot Builder > Build Hebrew voice bots and IVR (Interactive Voice Response) systems with speech-to-text, text-to-speech, and telephony integration for Israeli businesses. Use when user asks to \"build a Hebrew voice bot\", \"create an IVR in Hebrew\", \"Hebrew speech-to-text\", \"binui bot koli b'ivrit\", \"maarechet maane koli\", \"zihui dibur b'ivrit\", or \"Twilio Israel\". Covers OpenAI Whisper Hebrew, Goog… - **Type:** Skill - **Install:** `agentstack add skill-squadcodercom-squadcoder-hebrew-voice-bot-builder` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [squadcodercom](https://agentstack.voostack.com/s/squadcodercom) - **Installs:** 0 - **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [squadcodercom](https://github.com/squadcodercom) - **Source:** https://github.com/squadcodercom/squadcoder/tree/main/.squadcoder/skills/hebrew-voice-bot-builder - **Website:** https://squadcoder.com ## Install ```sh agentstack add skill-squadcodercom-squadcoder-hebrew-voice-bot-builder ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Hebrew Voice Bot Builder Build production-ready Hebrew voice bots and IVR systems for Israeli businesses. This skill covers the full voice pipeline: speech-to-text (STT), text-to-speech (TTS), IVR flow design, telephony integration, and Hebrew-specific challenges like accent handling and mixed Hebrew-English speech. ## Instructions ### Step 1: Choose Your Architecture Before building, decide on the voice bot architecture based on the use case: | Architecture | Best For | Components | |-------------|----------|------------| | IVR (keypad) | Simple menu navigation, payment lines, appointment scheduling | TTS + DTMF + telephony | | Voice bot (conversational) | Customer service, order status, FAQ handling | STT + LLM + TTS + telephony | | Voicemail transcription | Missed call handling, message routing | STT + notification pipeline | | Hybrid | Complex flows with both speech and keypad input | STT + TTS + DTMF + telephony | **Key decisions:** - **STT provider**: OpenAI `gpt-4o-transcribe` or `gpt-4o-mini-transcribe` (best Hebrew WER and lower latency than `whisper-1`, available via the OpenAI Realtime API for streaming), `whisper-large-v3-turbo` for self-host, ivrit-ai's Hebrew-tuned variants (`ivrit-ai/whisper-large-v3-turbo-ct2`) for the best open Hebrew WER, Google Cloud STT (low latency), Azure Speech (enterprise features). The legacy `whisper-1` API is still supported but `gpt-4o-transcribe` is the current default for Hebrew. - **TTS provider — split by use case**: - **Real-time / streaming (voice agents, IVR, live conversation)**: OpenAI Realtime API (`gpt-4o-realtime`) — native multilingual speech-to-speech including Hebrew, the 2026 default for sub-500ms turn-taking. Inworld TTS-1.5 / Realtime TTS-2 — explicitly lists Hebrew, sub-130ms P90 latency, designed for live conversation. Deepdub Phantom X 3.2 — Israeli company, real-time Hebrew with emotive eTTS, launched March 2026. ElevenLabs `eleven_flash_v2_5` — WebSocket-capable but Hebrew quality is weak, use only when latency dominates over quality. - **Offline / max quality (audiobooks, voicemail playback, batch generation)**: ElevenLabs `eleven_v3` — best Hebrew quality ElevenLabs offers, supports Hebrew (heb) among 70+ languages but **NO WebSocket / streaming API**, REST only. Deepdub Phantom X 3.2 also serves this track with emotional control. - **Fallbacks**: Azure Neural TTS (`he-IL-HilaNeural`, `he-IL-AvriNeural`), Google Cloud TTS Wavenet (`he-IL-Wavenet-A/B`), Amazon Polly Hebrew (Avri only, no neural Hebrew voice as of Apr 2026, verify before relying on it). ElevenLabs Multilingual v2 lists Hebrew in its 29-language table but real-world Hebrew quality is mediocre; Turbo v2.5 was not designed with Hebrew in mind. - **Telephony**: Twilio (largest Israeli number inventory), Vonage (competitive pricing for Israel). - **Hosting**: Cloud functions for low-volume, dedicated servers for high-volume. - **Recording-consent disclosure**: Israeli outbound automated calls must disclose recording at the start of the call (`השיחה מוקלטת`). Privacy Protection Law Amendment 13 (in force Aug 2025) tightens consent requirements for biometric voice data. ### Step 2: Hebrew Speech-to-Text (STT) #### OpenAI Whisper (Recommended for Accuracy) Whisper provides the best Hebrew transcription accuracy, especially for mixed Hebrew-English speech common in Israeli tech environments. ```python import openai client = openai.OpenAI() def transcribe_hebrew(audio_file_path: str) -> str: """Transcribe Hebrew audio using OpenAI Whisper.""" with open(audio_file_path, "rb") as audio_file: transcript = client.audio.transcriptions.create( model="whisper-1", file=audio_file, language="he", # Force Hebrew language detection response_format="text", ) return transcript def transcribe_hebrew_with_timestamps(audio_file_path: str) -> dict: """Transcribe with word-level timestamps for subtitle generation.""" with open(audio_file_path, "rb") as audio_file: transcript = client.audio.transcriptions.create( model="whisper-1", file=audio_file, language="he", response_format="verbose_json", timestamp_granularities=["word"], ) return transcript ``` **Whisper Hebrew tips:** - Set `language="he"` explicitly to avoid misdetecting Hebrew as Arabic - For mixed Hebrew-English, let Whisper auto-detect (omit the language parameter) and post-process - Whisper handles niqqud-free text well (standard for modern Hebrew) - Audio quality matters: 16kHz+ sample rate, mono channel, WAV or FLAC preferred - Maximum file size: 25MB. For longer recordings, split into segments #### Google Cloud Speech-to-Text Lower latency than Whisper, suitable for real-time voice bots. ```python from google.cloud import speech_v1 def transcribe_hebrew_google(audio_content: bytes) -> str: """Transcribe Hebrew audio using Google Cloud STT.""" client = speech_v1.SpeechClient() audio = speech_v1.RecognitionAudio(content=audio_content) config = speech_v1.RecognitionConfig( encoding=speech_v1.RecognitionConfig.AudioEncoding.LINEAR16, sample_rate_hertz=16000, language_code="he-IL", # Enable automatic punctuation for Hebrew enable_automatic_punctuation=True, # Model optimized for phone calls model="phone_call", # Enable word-level confidence scores enable_word_confidence=True, ) response = client.recognize(config=config, audio=audio) results = [] for result in response.results: results.append(result.alternatives[0].transcript) return " ".join(results) def stream_transcribe_hebrew(audio_generator): """Real-time streaming transcription for live phone calls.""" client = speech_v1.SpeechClient() config = speech_v1.StreamingRecognitionConfig( config=speech_v1.RecognitionConfig( encoding=speech_v1.RecognitionConfig.AudioEncoding.MULAW, sample_rate_hertz=8000, # Standard phone audio language_code="he-IL", model="phone_call", enable_automatic_punctuation=True, ), interim_results=True, # Get partial results for faster response ) streaming_config = speech_v1.StreamingRecognizeRequest( streaming_config=config ) def request_generator(): yield streaming_config for chunk in audio_generator: yield speech_v1.StreamingRecognizeRequest(audio_content=chunk) responses = client.streaming_recognize(requests=request_generator()) for response in responses: for result in response.results: if result.is_final: yield result.alternatives[0].transcript ``` #### Azure Speech Services Enterprise-grade with custom model training for domain-specific Hebrew vocabulary. ```python import azure.cognitiveservices.speech as speechsdk def transcribe_hebrew_azure(audio_file_path: str) -> str: """Transcribe Hebrew audio using Azure Speech Services.""" speech_config = speechsdk.SpeechConfig( subscription="YOUR_AZURE_KEY", region="westeurope", # Closest region to Israel ) speech_config.speech_recognition_language = "he-IL" audio_config = speechsdk.AudioConfig(filename=audio_file_path) recognizer = speechsdk.SpeechRecognizer( speech_config=speech_config, audio_config=audio_config, ) result = recognizer.recognize_once() if result.reason == speechsdk.ResultReason.RecognizedSpeech: return result.text elif result.reason == speechsdk.ResultReason.NoMatch: return "" else: raise RuntimeError(f"Speech recognition failed: {result.reason}") ``` Consult `references/hebrew-stt-models.md` for a detailed comparison of STT providers with accuracy benchmarks. ### Step 3: Hebrew Text-to-Speech (TTS) #### Google Cloud TTS (Recommended for Natural Sound) ```python from google.cloud import texttospeech def synthesize_hebrew(text: str, output_path: str, voice_gender: str = "female") -> None: """Convert Hebrew text to speech using Google Cloud TTS.""" client = texttospeech.TextToSpeechClient() input_text = texttospeech.SynthesisInput(text=text) # Available Hebrew voices voice_name_map = { "female": "he-IL-Wavenet-A", # Female, high quality "male": "he-IL-Wavenet-B", # Male, high quality "female_standard": "he-IL-Standard-A", # Female, lower cost "male_standard": "he-IL-Standard-B", # Male, lower cost } voice = texttospeech.VoiceSelectionParams( language_code="he-IL", name=voice_name_map.get(voice_gender, "he-IL-Wavenet-A"), ) audio_config = texttospeech.AudioConfig( audio_encoding=texttospeech.AudioEncoding.MP3, speaking_rate=1.0, # 0.5 to 2.0, adjust for clarity pitch=0.0, # -20.0 to 20.0 semitones ) response = client.synthesize_speech( input=input_text, voice=voice, audio_config=audio_config ) with open(output_path, "wb") as out: out.write(response.audio_content) ``` #### Amazon Polly Hebrew Cost-effective for high-volume TTS needs. ```python import boto3 def synthesize_hebrew_polly(text: str, output_path: str) -> None: """Convert Hebrew text to speech using Amazon Polly.""" polly = boto3.client("polly", region_name="eu-west-1") response = polly.synthesize_speech( Text=text, OutputFormat="mp3", VoiceId="Avri", # Hebrew male standard voice (no neural Hebrew voice exists) Engine="standard", # Standard engine (neural not available for Hebrew) LanguageCode="he-IL", ) with open(output_path, "wb") as out: out.write(response["AudioStream"].read()) ``` #### Azure Neural TTS Highest quality Hebrew voices with SSML support for fine-grained control. ```python import azure.cognitiveservices.speech as speechsdk def synthesize_hebrew_azure(text: str, output_path: str) -> None: """Convert Hebrew text to speech using Azure Neural TTS.""" speech_config = speechsdk.SpeechConfig( subscription="YOUR_AZURE_KEY", region="westeurope", ) # Hebrew neural voices speech_config.speech_synthesis_voice_name = "he-IL-HilaNeural" # Female # Alternative: "he-IL-AvriNeural" for male voice audio_config = speechsdk.AudioConfig(filename=output_path) synthesizer = speechsdk.SpeechSynthesizer( speech_config=speech_config, audio_config=audio_config, ) result = synthesizer.speak_text(text) if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted: raise RuntimeError(f"Speech synthesis failed: {result.reason}") def synthesize_hebrew_ssml(ssml: str, output_path: str) -> None: """ Synthesize Hebrew speech with SSML for fine control. Example SSML for IVR prompt: ברוכים הבאים לשירות הלקוחות. לתמיכה טכנית, הקישו 1. למכירות, הקישו 2. """ speech_config = speechsdk.SpeechConfig( subscription="YOUR_AZURE_KEY", region="westeurope", ) audio_config = speechsdk.AudioConfig(filename=output_path) synthesizer = speechsdk.SpeechSynthesizer( speech_config=speech_config, audio_config=audio_config, ) result = synthesizer.speak_ssml(ssml) if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted: raise RuntimeError(f"SSML synthesis failed: {result.reason}") ``` ### Step 4: IVR Menu Design for Israeli Businesses Israeli IVR systems have specific conventions that differ from US/European patterns. #### Business Hours Routing Israeli business week is Sunday through Thursday. IVR systems must account for this: ```python from datetime import datetime import pytz ISRAEL_TZ = pytz.timezone("Asia/Jerusalem") def get_business_status() -> dict: """Determine current business status for IVR routing.""" now = datetime.now(ISRAEL_TZ) day = now.weekday() # 0=Monday, 6=Sunday hour = now.hour # Israeli business days: Sunday (6) through Thursday (3) # Friday (4): half day until ~13:00 # Saturday (5): closed (Shabbat) if day == 5: # Saturday (Shabbat) return { "status": "closed", "reason": "shabbat", "message_he": "שלום, אנחנו סגורים בשבת. נחזור אליכם ביום ראשון.", "next_open": "Sunday 9:00", } elif day == 4: # Friday if hour dict: """ Process a voicemail recording: transcribe, classify, and route. Args: audio_path: Path to the voicemail audio file caller_number: Caller's phone number (+972...) Returns: Processed voicemail with transcript and routing info """ # Step 1: Transcribe using Whisper (best Hebrew accuracy) transcript = transcribe_hebrew(audio_path) # Step 2: Detect language (Hebrew, English, or mixed) language = detect_voicemail_language(transcript) # Step 3: Classify intent intent = classify_voicemail_intent(transcript) # Step 4: Extract key entities entities = extract_voicemail_entities(transcript) result = { "caller": caller_number, "timestamp": datetime.now().isoformat(), "transcript": transcript, "language": language, "intent": intent, "entities": entities, "audio_path": audio_path, "duration_seconds": get_audio_duration(audio_path), } # Step 5: Route based on intent result["routing"] = route_voicemail(intent, entities) return result def detect_voicemail_language(text: str) -> str: """Detect whether voicemail is Hebrew, English, or mixed.""" hebrew_chars = sum(1 for c in text if "\u0590" 0.7: return "hebrew" elif hebrew_ratio str: """Classify voicemail intent based on Hebrew keywords.""" for intent, keywords in VOICEMAIL_INTENTS.items(): if any(keyword in transcript for keyword in keywords): return intent return "general" ``` ### Step 6: Mixed Language Handling (Hebrew-English) Israeli tech professionals frequently switch between Hebrew and English mid-sentence (code-switching). Voice bots must handle this gracefully. ```python def handle_mixed_speech(audio_path: str) -> dict: """ Handle mixed Hebrew-English speech common in Israeli tech. Strategy: Use Whisper without language hint for auto-detection, then post-process to normalize mixed output. """ client = openai.OpenAI() with open(audio_path, "rb") as f: # Omit language parameter to let Whisper handle code-switching transcript = client.audio.transcriptions.create( model="whisper-1", file=f, response_format="verbose_json", ) segments = [] for segment in transcript.segments: text = segment["text"] lang = detect_segment_language(text) segments.append({ "text": text, "language": lang, "start": segment["start"], "end": segment["end"], }) return { "full_transcript": transcript.text, "segments": segments, "detected_languages": list(set(s["language"] for s in segments)), } # Common Hebrew-English tech phrases that Whisper may mishandle HEBREW_ENGLISH_CORRECTIONS = { "דיפלוי": "deploy", # Hebrew-accented English "פושׁ": "push", "קומיט": "commit", "סרבר": "server", "באג": "bug", "פיצ'ר": "feature", "אפליקציה": "application", "דאטהבייס": "database", } ``` ### Step 7: Phone Integration (Twilio) #### Setting Up Twilio with Israeli Numbers (+972) ```python from twilio.rest import Client from twilio.twiml.voice_response import VoiceResponse, Gather TWILIO_ACCOUNT_S … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [squadcodercom](https://github.com/squadcodercom) - **Source:** [squadcodercom/squadcoder](https://github.com/squadcodercom/squadcoder) - **License:** MIT - **Homepage:** https://squadcoder.com Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** yes - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-squadcodercom-squadcoder-hebrew-voice-bot-builder - Seller: https://agentstack.voostack.com/s/squadcodercom - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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