Support English-only models

faster_whisper/transcribe.py

@@ -1,5 +1,4 @@
 import collections
-import os
 import zlib
 
 import ctranslate2
@@ -78,21 +77,10 @@ class WhisperModel:
             inter_threads=num_workers,
         )
 
-        with open(os.path.join(model_path, "vocabulary.txt")) as vocab_file:
-            vocab_size = sum(1 for _ in vocab_file)
-
-        is_multilingual = vocab_size == 51865
-        if not is_multilingual:
-            raise NotImplementedError(
-                "English-only models are currently not supported. "
-                "The underlying CTranslate2 implementation makes some assumptions about "
-                "the prompt format that are not compatible with English-only models. "
-                "This will be improved in a future version. "
-                "Please use a multilingual model for now."
-            )
-
         self.feature_extractor = FeatureExtractor()
-        self.tokenizer = tokenizers.Tokenizer.from_pretrained("openai/whisper-tiny")
+        self.tokenizer = tokenizers.Tokenizer.from_pretrained(
+            "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
+        )
         self.eot_id = self.tokenizer.token_to_id("<|endoftext|>")
         self.timestamp_begin_id = self.tokenizer.token_to_id("<|notimestamps|>") + 1
         self.input_stride = 2
@@ -154,11 +142,15 @@ class WhisperModel:
         features = self.feature_extractor(audio)
 
         if language is None:
-            segment = self.get_segment(features)
-            input = self.get_input(segment)
-            results = self.model.detect_language(input)
-            language_token, language_probability = results[0][0]
-            language = language_token[2:-2]
+            if not self.model.is_multilingual:
+                language = "en"
+                language_probability = 1
+            else:
+                segment = self.get_segment(features)
+                input = self.get_input(segment)
+                results = self.model.detect_language(input)
+                language_token, language_probability = results[0][0]
+                language = language_token[2:-2]
         else:
             language_probability = 1
 
@@ -353,11 +345,15 @@ class WhisperModel:
             prompt.append(self.tokenizer.token_to_id("<|startofprev|>"))
             prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
 
-        prompt += [
-            self.tokenizer.token_to_id("<|startoftranscript|>"),
-            self.tokenizer.token_to_id("<|%s|>" % language),
-            self.tokenizer.token_to_id("<|%s|>" % task),
-        ]
+        prompt.append(self.tokenizer.token_to_id("<|startoftranscript|>"))
+
+        if self.model.is_multilingual:
+            prompt.extend(
+                [
+                    self.tokenizer.token_to_id("<|%s|>" % language),
+                    self.tokenizer.token_to_id("<|%s|>" % task),
+                ]
+            )
 
         if without_timestamps:
             prompt.append(self.tokenizer.token_to_id("<|notimestamps|>"))