refactor multilingual option (#1148)

* Added test for `multilingual` option with english-german audio
* removed `output_language` argument as it is redundant, you can get the same functionality with `task="translate"`
* use the correct `encoder_output` for language detection in sequential transcription
* enabled `multilingual` functionality for batched inference

faster_whisper/transcribe.py

@@ -93,7 +93,6 @@ class TranscriptionOptions:
     prepend_punctuations: str
     append_punctuations: str
     multilingual: bool
-    output_language: Optional[str]
     max_new_tokens: Optional[int]
     clip_timestamps: Union[str, List[float]]
     hallucination_silence_threshold: Optional[float]
@@ -210,10 +209,21 @@ class BatchedInferencePipeline:
             )
 
         encoder_output = self.model.encode(features)
+        prompts = [prompt.copy() for _ in range(batch_size)]
+
+        if options.multilingual:
+            language_tokens = [
+                tokenizer.tokenizer.token_to_id(segment_langs[0][0])
+                for segment_langs in self.model.model.detect_language(encoder_output)
+            ]
+            language_token_index = prompt.index(tokenizer.language)
+
+            for i, language_token in enumerate(language_tokens):
+                prompts[i][language_token_index] = language_token
 
         results = self.model.model.generate(
             encoder_output,
-            [prompt] * batch_size,
+            prompts,
             beam_size=options.beam_size,
             patience=options.patience,
             length_penalty=options.length_penalty,
@@ -279,7 +289,6 @@ class BatchedInferencePipeline:
         prepend_punctuations: str = "\"'“¿([{-",
         append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
         multilingual: bool = False,
-        output_language: Optional[str] = None,
         vad_filter: bool = True,
         vad_parameters: Optional[Union[dict, VadOptions]] = None,
         max_new_tokens: Optional[int] = None,
@@ -322,6 +331,7 @@ class BatchedInferencePipeline:
                 with the next word
             append_punctuations: If word_timestamps is True, merge these punctuation symbols
                 with the previous word
+            multilingual: Perform language detection on every segment.
             vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
                 without speech. This step is using the Silero VAD model
                 https://github.com/snakers4/silero-vad.
@@ -360,10 +370,6 @@ class BatchedInferencePipeline:
                 Arg has effect only if condition_on_previous_text is True. Set at 0.5
             prefix: Optional text to provide as a prefix at the beginning of each window.
             max_initial_timestamp: The initial timestamp cannot be later than this, set at 0.0.
-            multilingual: If True, perform transcription on multilingual videos. Set as False.
-            output_language: Valid only if multilingual is set to True.
-                Specifies the string representing the output language. One of
-                'en' (English) or 'hybrid' (code-switched transcription). set as None.
             hallucination_silence_threshold: Optional[float]
                 When word_timestamps is True, skip silent periods longer than this threshold
                 (in seconds) when a possible hallucination is detected. set as None.
@@ -376,6 +382,13 @@ class BatchedInferencePipeline:
 
         sampling_rate = self.model.feature_extractor.sampling_rate
 
+        if multilingual and not self.model.model.is_multilingual:
+            self.model.logger.warning(
+                "The current model is English-only but the multilingual parameter is set to"
+                "True; setting to False instead."
+            )
+            multilingual = False
+
         if not isinstance(audio, np.ndarray):
             audio = decode_audio(audio, sampling_rate=sampling_rate)
         duration = audio.shape[0] / sampling_rate
@@ -498,8 +511,7 @@ class BatchedInferencePipeline:
             condition_on_previous_text=False,
             clip_timestamps=clip_timestamps,
             prompt_reset_on_temperature=0.5,
-            multilingual=False,
-            output_language=None,
+            multilingual=multilingual,
             without_timestamps=without_timestamps,
             max_initial_timestamp=0.0,
         )
@@ -721,7 +733,6 @@ class WhisperModel:
         prepend_punctuations: str = "\"'“¿([{-",
         append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
         multilingual: bool = False,
-        output_language: Optional[str] = None,
         vad_filter: bool = False,
         vad_parameters: Optional[Union[dict, VadOptions]] = None,
         max_new_tokens: Optional[int] = None,
@@ -781,12 +792,7 @@ class WhisperModel:
             with the next word
           append_punctuations: If word_timestamps is True, merge these punctuation symbols
             with the previous word
-          multilingual: If True, perform transcription on multilingual videos
-            and return the transcript based
-            on the 'output_language' flag.
-          output_language: Valid only if multilingual is set to True.
-            Specifies the string representing the output language. One of
-            'en' (English) or 'hybrid' (code-switched transcription).
+          multilingual: Perform language detection on every segment.
           vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
             without speech. This step is using the Silero VAD model
             https://github.com/snakers4/silero-vad.
@@ -817,6 +823,13 @@ class WhisperModel:
 
         sampling_rate = self.feature_extractor.sampling_rate
 
+        if multilingual and not self.model.is_multilingual:
+            self.logger.warning(
+                "The current model is English-only but the multilingual parameter is set to"
+                "True; setting to False instead."
+            )
+            multilingual = False
+
         if not isinstance(audio, np.ndarray):
             audio = decode_audio(audio, sampling_rate=sampling_rate)
 
@@ -863,13 +876,6 @@ class WhisperModel:
         encoder_output = None
         all_language_probs = None
 
-        # setting output_language for multilingual videos
-        if multilingual:
-            if output_language is None:
-                output_language = "en"
-            elif output_language not in ["en", "hybrid"]:
-                raise ValueError("Output language needs to be one of 'en'/'hybrid'.")
-
         # detecting the language if not provided
         if language is None:
             if not self.model.is_multilingual:
@@ -949,7 +955,6 @@ class WhisperModel:
             prepend_punctuations=prepend_punctuations,
             append_punctuations=append_punctuations,
             multilingual=multilingual,
-            output_language=output_language,
             max_new_tokens=max_new_tokens,
             clip_timestamps=clip_timestamps,
             hallucination_silence_threshold=hallucination_silence_threshold,
@@ -1139,27 +1144,17 @@ class WhisperModel:
 
             previous_tokens = all_tokens[prompt_reset_since:]
 
-            if encoder_output is None:
+            if seek > 0 or encoder_output is None:
                 encoder_output = self.encode(segment)
 
-            # Perform language detection at every segment to update task based on output language,
-            # if the language is english, task is transcribe,
-            # else the task is translate to english (default)
-            # or transcribe if 'output_language' is 'hybrid'.
             if options.multilingual:
                 results = self.model.detect_language(encoder_output)
                 language_token, language_probability = results[0][0]
                 language = language_token[2:-2]
-                if options.output_language == "en" and language != "en":
-                    task = "translate"
-                else:
-                    task = "transcribe"
 
-                # Update tokenizer based on task and language
-                tokenizer.task = tokenizer.tokenizer.token_to_id(f"<|{task}|>")
                 tokenizer.language = tokenizer.tokenizer.token_to_id(language_token)
                 tokenizer.language_code = language
-            # Update prompt based on task and language
+
             prompt = self.get_prompt(
                 tokenizer,
                 previous_tokens,
@@ -1168,9 +1163,6 @@ class WhisperModel:
                 hotwords=options.hotwords,
             )
 
-            if seek > 0 or encoder_output is None:
-                encoder_output = self.encode(segment)
-
             (
                 result,
                 avg_logprob,